From 2d18a8f7fc00990b4b2f8780e998e322478dfbd3 Mon Sep 17 00:00:00 2001 From: svmiller Date: Thu, 30 Jan 2025 11:50:17 +0100 Subject: [PATCH] fsfsfsfs --- R/linloess_plot.R | 11 ++++++----- docs/pkgdown.yml | 2 +- docs/reference/index.html | 2 +- docs/reference/linloess_plot.html | 26 +++++++++++++------------- docs/search.json | 2 +- man/linloess_plot.Rd | 16 ++++++++-------- 6 files changed, 30 insertions(+), 29 deletions(-) diff --git a/R/linloess_plot.R b/R/linloess_plot.R index a98b259..0c73d49 100644 --- a/R/linloess_plot.R +++ b/R/linloess_plot.R @@ -43,7 +43,7 @@ #' @param suppress_warning logical, defaults to \code{TRUE}. If \code{TRUE}, #' the plot suppresses assorted warnings from the LOESS smoother that would #' otherwise be cautioning you about things your eyes could otherwise see. -#' @param ... optional parameters, passed to the scatterplot +#' @param ... optional parameters, passed to the scatterplot in \code{linloess_plot()} #' (\code{geom_point()}) component of this function. Useful if you want to make #' the smoothers more legible against the points. #' @@ -114,11 +114,12 @@ linloess_plot <- function(mod, resid = TRUE, smoother = "loess", #' Print method for class 'linloess' #' -#' @param llplot a ggplot object with this special 'linloess' class +#' @param x a ggplot object with this special 'linloess' class +#' @param ... Additional arguments in the context of the print function (not used) #' @keywords internal #' @rdname linloess_plot #' @export -print.linloess <- function(llplot) { - class(llplot) <- setdiff(class(llplot), "linloess") - suppressWarnings(print(llplot)) +print.linloess <- function(x, ...) { + class(x) <- setdiff(class(x), "linloess") + suppressWarnings(print(x, ...)) } diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 2632909..4bf47f7 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -2,7 +2,7 @@ pandoc: 3.1.1 pkgdown: 2.1.1 pkgdown_sha: ~ articles: {} -last_built: 2025-01-30T10:31Z +last_built: 2025-01-30T10:49Z urls: reference: http://svmiller.com/reference article: http://svmiller.com/articles diff --git a/docs/reference/index.html b/docs/reference/index.html index b983bc6..d3ae9d5 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -119,7 +119,7 @@

Functionslinloess_plot() print(<linloess>) -
Compare Linear Smoother to LOESS Smoother for Your OLS Model
+
Compare Linear Smoother to LOESS Smoother for Your Linear Model
make_perclab() diff --git a/docs/reference/linloess_plot.html b/docs/reference/linloess_plot.html index d8d2d37..6f2975d 100644 --- a/docs/reference/linloess_plot.html +++ b/docs/reference/linloess_plot.html @@ -1,19 +1,19 @@ -Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot • stevemiscCompare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot • stevemisc Skip to contents @@ -44,19 +44,19 @@
-

Compare Linear Smoother to LOESS Smoother for Your OLS Model

+

Compare Linear Smoother to LOESS Smoother for Your Linear Model

Source: R/linloess_plot.R
linloess_plot.Rd

linloess_plot() provides a visual diagnostic of the -linearity assumption of the OLS model. Provided an OLS model fit by +linearity assumption of the OLS model. Provided a linear model fit by lm() in base R, the function extracts the model frame and creates a faceted scatterplot. For each facet, a linear smoother and LOESS smoother are estimated over the points. Users who run this function can assess just how much the linear smoother and LOESS smoother diverge. The more they -diverge, the more the user can determine how much the OLS model is a good +diverge, the more the user can determine how much the linear model is a good fit as specified. The plot will also point to potential outliers that may need further consideration.

@@ -82,7 +82,7 @@

Argumentsmod

-

a fitted OLS model

+

a fitted model, ideally a simple linear model

resid
@@ -116,7 +116,7 @@

Arguments... -

additional arguments (ignored)

+

Additional arguments in the context of the print function (not used)

x
@@ -130,9 +130,9 @@

Value

+depending your data and whether you have disabled the warnings in the +function. I think these to be fine the extent to which this is really just a +visual aid and an informal diagnostic for the linearity assumption.

Details

diff --git a/docs/search.json b/docs/search.json index ef16f5f..334b830 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"http://svmiller.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Steve Miller. Author, maintainer. Ben Bolker. Contributor. Dave Armstrong. Contributor. John Fox. Contributor. Winston Chang. Contributor. Brian Ripley. Contributor. Bill Venables. Contributor. Pascal van Kooten. Contributor. Gerko Vink. Contributor. Paul Williamson. Contributor. Andreas Beger. Contributor. Vincent Arel-Bundock. Contributor. Grant McDermott. Contributor. Hadley Wickham. Contributor.","code":""},{"path":"http://svmiller.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller S (2025). stevemisc: Steve's Miscellaneous Functions. R package version 1.8.9.","code":"@Manual{, title = {stevemisc: Steve's Miscellaneous Functions}, author = {Steve Miller}, year = {2025}, note = {R package version 1.8.9}, }"},{"path":"http://svmiller.com/index.html","id":"steves-miscellaneous-functions","dir":"","previous_headings":"","what":"Steve's Miscellaneous Functions","title":"Steve's Miscellaneous Functions","text":"{stevemisc} R package includes various functions tools written years assist research, teaching, public presentations (.e. stuff put blog). offer public release 1) vain think want entire, eponymous ecosystem R programming language (.e. “steveverse”) 2) think tools broadly useful users ’m trying bundle things offer (prominently {steveproj}). Namely, {stevemisc} offers tools assist data organization, data presentation, data recoding, data simulation.","code":""},{"path":"http://svmiller.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Steve's Miscellaneous Functions","text":"can install CRAN. can install development version {stevemisc} Github via devtools package. suppose using remotes package work well.","code":"install.packages(\"stevemisc\") devtools::install_github(\"svmiller/stevemisc\")"},{"path":"http://svmiller.com/reference/at.html","id":null,"dir":"Reference","previous_headings":"","what":"Scoped Helper Verbs — center_at","title":"Scoped Helper Verbs — center_at","text":"Scoped helper verbs included R Documentation file allow targeted commands specified columns. also rename ensuing output conform preferred style. commands multiple explained details section .","code":""},{"path":"http://svmiller.com/reference/at.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scoped Helper Verbs — center_at","text":"","code":"center_at(data, x, prefix = \"c\", na = TRUE, .by = NULL) diff_at(data, x, o = 1, prefix = \"d\", .by = NULL) group_mean_center_at( data, x, mean_prefix = \"mean\", prefix = \"b\", na = TRUE, .by ) lag_at(data, x, prefix = \"l\", o = 1, .by = NULL) log_at(data, x, prefix = \"ln\", plus_1 = FALSE) mean_at(data, x, prefix = \"mean\", na = TRUE, .by = NULL) r1sd_at(data, x, prefix = \"s\", na = TRUE, .by = NULL) r2sd_at(data, x, prefix = \"z\", na = TRUE, .by = NULL) rewb_at(data, x, w_prefix = \"w\", b_prefix = \"b\", .by)"},{"path":"http://svmiller.com/reference/at.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scoped Helper Verbs — center_at","text":"data data frame x vector, likely data frame prefix Allows user rename prefix new variables. function defaults (see details section). na logical whether missing values ignored creation means re-scaled variables. Defaults TRUE (.e. pass /remove missing observations). applicable diff_at, lag_at, log_at. .selection columns group operation. Defaults NULL. eventually become standard feature functions operator moves beyond experimental dplyr. argument applicable log_at () optional functions except group_mean_center_at. group_mean_center_at must something specified grouped mean-centering. o order lags calculating differences lags diff_at lag_at. Applicable functions. mean_prefix Applicable group_mean_center_at. Specifies prefix (assumed) total population mean variables. Default \"mean\", though user can change see fit. plus_1 Applicable log_at. TRUE, adds 1 variables prior log transformation. FALSE, performs logarithmic transformation variables matter whether 0 occurs (.e. 0s come back -Inf). Defaults FALSE. w_prefix Applicable rewb_at, specifies prefix -called \"within\" variables created procedure. Defaults \"w\". b_prefix Applicable rewb_at, specifies prefix -called \"\" variables created procedure. Defaults \"b\".","code":""},{"path":"http://svmiller.com/reference/at.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scoped Helper Verbs — center_at","text":"function returns set new vectors data frame performing relevant functions. new vectors distinct prefixes corresponding action performed .","code":""},{"path":"http://svmiller.com/reference/at.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scoped Helper Verbs — center_at","text":"center_at wrapper mutate_at rename_at dplyr. takes supplied vectors effectively centers mean. renames new variables prefix c_. default prefix (\"c\") can changed way argument function. diff_at wrapper mutate across dplyr. takes supplied vectors creates differences previous value recorded . renames new variables prefix d_ (case first difference), something like d2_ case second differences, d3_ case third differences (). exact prefix depends o argument, communicates order lags want. defaults 1. default prefix (\"d\") can changed way argument function, though naming convention omit numerical prefix first differences. group_mean_center_at wrapper mutate across dplyr. takes supplied vectors centers (assumed) group mean variables (assumed) total population mean variables provided . returns new variables prefix, whose default b_. prefix communicates, , kind \"\" variable panel model context, juxtaposition \"within\" variables panel model context. lag_at wrapper mutate across dplyr. takes supplied vector(s) creates lag variables . new variables prefix l[o]_ o corresponds order lag (specified argument function, defaults 1). default prefix (\"l\") can changed way another argument function. log_at wrapper mutate across dplyr. takes supplied vectors creates variable takes natural logarithmic transformation . renames new variables prefix ln_. default prefix (\"ln\") can changed way argument function. Users can optionally specify want add 1 vector taking natural logarithm, popular thing positive reals naturally occurring zeroes. mean_at wrapper mutate across dplyr. takes supplied vectors creates variable communicating mean variable. renames new variables prefix mean_. default prefix (\"mean\") can changed way argument function. r1sd_at wrapper mutate across dplyr. rescales supplied vectors new vectors renames vectors prefix s_. Note rescaling just one standard deviation two. default prefix (\"s\") can changed way argument function. r2sd_at wrapper mutate across dplyr. rescales supplied vectors new vectors renames vectors prefix z_. Note rescaling two standard deviations one. default prefix (\"z\") can changed way argument function. rewb_at wrapper routines done mean_at, group_mean_center_at, center_at package. implicitly assumes data panel runs three routines order create -called \"\" \"within\" variables \"random effects, within-\" analysis. Means calculated based available data, na argument available function. function fail presence variables data matching routine wants create. functions, except lag_at, fail absence character vector length one. intended work across multiple columns instead just one. wanting create one new variable, think using dplyr verb .","code":""},{"path":"http://svmiller.com/reference/at.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scoped Helper Verbs — center_at","text":"","code":"set.seed(8675309) Example <- data.frame(category = c(rep(\"A\", 5), rep(\"B\", 5), rep(\"C\", 5)), x = runif(15), y = runif(15), z = sample(1:20, 15, replace=TRUE)) my_vars <- c(\"x\", \"y\", \"z\") center_at(Example, my_vars) #> category x y z c_x c_y c_z #> 1 A 0.1594836 0.91822046 9 -0.45270578 0.38488743 -4 #> 2 A 0.4781883 0.71636154 19 -0.13400109 0.18302851 6 #> 3 A 0.7647987 0.20624914 15 0.15260928 -0.32708389 2 #> 4 A 0.7696877 0.81691683 20 0.15749826 0.28358381 7 #> 5 A 0.2685485 0.71585943 14 -0.34364092 0.18252640 1 #> 6 B 0.6730459 0.06062449 14 0.06085649 -0.47270853 1 #> 7 B 0.9787908 0.84710058 17 0.36660137 0.31376756 4 #> 8 B 0.8463270 0.84676044 8 0.23413755 0.31342741 -5 #> 9 B 0.8566562 0.33261085 16 0.24446673 -0.20072218 3 #> 10 B 0.4451601 0.55965050 16 -0.16702927 0.02631747 3 #> 11 C 0.8382325 0.66946933 12 0.22604312 0.13613631 -1 #> 12 C 0.5833169 0.25463848 18 -0.02887250 -0.27869455 5 #> 13 C 0.5109512 0.07917477 5 -0.10123826 -0.45415826 -8 #> 14 C 0.2601681 0.15996809 6 -0.35202128 -0.37336494 -7 #> 15 C 0.7494857 0.81639049 6 0.13729632 0.28305746 -7 diff_at(Example, my_vars) #> category x y z d_x d_y d_z #> 1 A 0.1594836 0.91822046 9 NA NA NA #> 2 A 0.4781883 0.71636154 19 0.318704692 -0.2018589161 10 #> 3 A 0.7647987 0.20624914 15 0.286610370 -0.5101124048 -4 #> 4 A 0.7696877 0.81691683 20 0.004888979 0.6106676969 5 #> 5 A 0.2685485 0.71585943 14 -0.501139179 -0.1010574063 -6 #> 6 B 0.6730459 0.06062449 14 0.404497413 -0.6552349348 0 #> 7 B 0.9787908 0.84710058 17 0.305744875 0.7864760908 3 #> 8 B 0.8463270 0.84676044 8 -0.132463813 -0.0003401469 -9 #> 9 B 0.8566562 0.33261085 16 0.010329181 -0.5141495881 8 #> 10 B 0.4451601 0.55965050 16 -0.411496004 0.2270396501 0 #> 11 C 0.8382325 0.66946933 12 0.393072386 0.1098188350 -4 #> 12 C 0.5833169 0.25463848 18 -0.254915616 -0.4148308551 6 #> 13 C 0.5109512 0.07917477 5 -0.072365765 -0.1754637090 -13 #> 14 C 0.2601681 0.15996809 6 -0.250783015 0.0807933176 1 #> 15 C 0.7494857 0.81639049 6 0.489317597 0.6564223976 0 diff_at(Example, my_vars, o=3) #> category x y z d3_x d3_y d3_z #> 1 A 0.1594836 0.91822046 9 NA NA NA #> 2 A 0.4781883 0.71636154 19 NA NA NA #> 3 A 0.7647987 0.20624914 15 NA NA NA #> 4 A 0.7696877 0.81691683 20 0.610204041 -0.1013036240 11 #> 5 A 0.2685485 0.71585943 14 -0.209639830 -0.0005021142 -5 #> 6 B 0.6730459 0.06062449 14 -0.091752786 -0.1456246441 -1 #> 7 B 0.9787908 0.84710058 17 0.209103110 0.0301837497 -3 #> 8 B 0.8463270 0.84676044 8 0.577778475 0.1309010091 -6 #> 9 B 0.8566562 0.33261085 16 0.183610243 0.2719863558 2 #> 10 B 0.4451601 0.55965050 16 -0.533630636 -0.2874500849 -1 #> 11 C 0.8382325 0.66946933 12 -0.008094437 -0.1772911029 4 #> 12 C 0.5833169 0.25463848 18 -0.273339234 -0.0779723700 2 #> 13 C 0.5109512 0.07917477 5 0.065791005 -0.4804757291 -11 #> 14 C 0.2601681 0.15996809 6 -0.578064396 -0.5095012465 -6 #> 15 C 0.7494857 0.81639049 6 0.166168816 0.5617520062 -12 lag_at(Example, my_vars) #> category x y z l1_x l1_y l1_z #> 1 A 0.1594836 0.91822046 9 NA NA NA #> 2 A 0.4781883 0.71636154 19 0.1594836 0.91822046 9 #> 3 A 0.7647987 0.20624914 15 0.4781883 0.71636154 19 #> 4 A 0.7696877 0.81691683 20 0.7647987 0.20624914 15 #> 5 A 0.2685485 0.71585943 14 0.7696877 0.81691683 20 #> 6 B 0.6730459 0.06062449 14 0.2685485 0.71585943 14 #> 7 B 0.9787908 0.84710058 17 0.6730459 0.06062449 14 #> 8 B 0.8463270 0.84676044 8 0.9787908 0.84710058 17 #> 9 B 0.8566562 0.33261085 16 0.8463270 0.84676044 8 #> 10 B 0.4451601 0.55965050 16 0.8566562 0.33261085 16 #> 11 C 0.8382325 0.66946933 12 0.4451601 0.55965050 16 #> 12 C 0.5833169 0.25463848 18 0.8382325 0.66946933 12 #> 13 C 0.5109512 0.07917477 5 0.5833169 0.25463848 18 #> 14 C 0.2601681 0.15996809 6 0.5109512 0.07917477 5 #> 15 C 0.7494857 0.81639049 6 0.2601681 0.15996809 6 lag_at(Example, my_vars, o=3) #> category x y z l1_x l2_x l3_x l1_y #> 1 A 0.1594836 0.91822046 9 NA NA NA NA #> 2 A 0.4781883 0.71636154 19 0.1594836 NA NA 0.91822046 #> 3 A 0.7647987 0.20624914 15 0.4781883 0.1594836 NA 0.71636154 #> 4 A 0.7696877 0.81691683 20 0.7647987 0.4781883 0.1594836 0.20624914 #> 5 A 0.2685485 0.71585943 14 0.7696877 0.7647987 0.4781883 0.81691683 #> 6 B 0.6730459 0.06062449 14 0.2685485 0.7696877 0.7647987 0.71585943 #> 7 B 0.9787908 0.84710058 17 0.6730459 0.2685485 0.7696877 0.06062449 #> 8 B 0.8463270 0.84676044 8 0.9787908 0.6730459 0.2685485 0.84710058 #> 9 B 0.8566562 0.33261085 16 0.8463270 0.9787908 0.6730459 0.84676044 #> 10 B 0.4451601 0.55965050 16 0.8566562 0.8463270 0.9787908 0.33261085 #> 11 C 0.8382325 0.66946933 12 0.4451601 0.8566562 0.8463270 0.55965050 #> 12 C 0.5833169 0.25463848 18 0.8382325 0.4451601 0.8566562 0.66946933 #> 13 C 0.5109512 0.07917477 5 0.5833169 0.8382325 0.4451601 0.25463848 #> 14 C 0.2601681 0.15996809 6 0.5109512 0.5833169 0.8382325 0.07917477 #> 15 C 0.7494857 0.81639049 6 0.2601681 0.5109512 0.5833169 0.15996809 #> l2_y l3_y l1_z l2_z l3_z #> 1 NA NA NA NA NA #> 2 NA NA 9 NA NA #> 3 0.91822046 NA 19 9 NA #> 4 0.71636154 0.91822046 15 19 9 #> 5 0.20624914 0.71636154 20 15 19 #> 6 0.81691683 0.20624914 14 20 15 #> 7 0.71585943 0.81691683 14 14 20 #> 8 0.06062449 0.71585943 17 14 14 #> 9 0.84710058 0.06062449 8 17 14 #> 10 0.84676044 0.84710058 16 8 17 #> 11 0.33261085 0.84676044 16 16 8 #> 12 0.55965050 0.33261085 12 16 16 #> 13 0.66946933 0.55965050 18 12 16 #> 14 0.25463848 0.66946933 5 18 12 #> 15 0.07917477 0.25463848 6 5 18 log_at(Example, my_vars) #> category x y z ln_x ln_y ln_z #> 1 A 0.1594836 0.91822046 9 -1.83581396 -0.08531777 2.197225 #> 2 A 0.4781883 0.71636154 19 -0.73775063 -0.33357029 2.944439 #> 3 A 0.7647987 0.20624914 15 -0.26814262 -1.57867043 2.708050 #> 4 A 0.7696877 0.81691683 20 -0.26177046 -0.20221798 2.995732 #> 5 A 0.2685485 0.71585943 14 -1.31472376 -0.33427146 2.639057 #> 6 B 0.6730459 0.06062449 14 -0.39594173 -2.80305628 2.639057 #> 7 B 0.9787908 0.84710058 17 -0.02143736 -0.16593584 2.833213 #> 8 B 0.8463270 0.84676044 8 -0.16684950 -0.16633746 2.079442 #> 9 B 0.8566562 0.33261085 16 -0.15471866 -1.10078209 2.772589 #> 10 B 0.4451601 0.55965050 16 -0.80932117 -0.58044280 2.772589 #> 11 C 0.8382325 0.66946933 12 -0.17645973 -0.40126992 2.484907 #> 12 C 0.5833169 0.25463848 18 -0.53902464 -1.36791047 2.890372 #> 13 C 0.5109512 0.07917477 5 -0.67148128 -2.53609759 1.609438 #> 14 C 0.2601681 0.15996809 6 -1.34642717 -1.83278093 1.791759 #> 15 C 0.7494857 0.81639049 6 -0.28836799 -0.20286250 1.791759 log_at(Example, my_vars, plus_1 = TRUE) #> category x y z ln_x ln_y ln_z #> 1 A 0.1594836 0.91822046 9 0.1479748 0.65139791 2.302585 #> 2 A 0.4781883 0.71636154 19 0.3908172 0.54020667 2.995732 #> 3 A 0.7647987 0.20624914 15 0.5680366 0.18751566 2.772589 #> 4 A 0.7696877 0.81691683 20 0.5708031 0.59714102 3.044522 #> 5 A 0.2685485 0.71585943 14 0.2378733 0.53991408 2.708050 #> 6 B 0.6730459 0.06062449 14 0.5146459 0.05885788 2.708050 #> 7 B 0.9787908 0.84710058 17 0.6824859 0.61361716 2.890372 #> 8 B 0.8463270 0.84676044 8 0.6131982 0.61343299 2.197225 #> 9 B 0.8566562 0.33261085 16 0.6187771 0.28714006 2.833213 #> 10 B 0.4451601 0.55965050 16 0.3682201 0.44446176 2.833213 #> 11 C 0.8382325 0.66946933 12 0.6088045 0.51250581 2.564949 #> 12 C 0.5833169 0.25463848 18 0.4595220 0.22684747 2.944439 #> 13 C 0.5109512 0.07917477 5 0.4127394 0.07619665 1.791759 #> 14 C 0.2601681 0.15996809 6 0.2312452 0.14839249 1.945910 #> 15 C 0.7494857 0.81639049 6 0.5593219 0.59685128 1.945910 mean_at(Example, my_vars) #> category x y z mean_x mean_y mean_z #> 1 A 0.1594836 0.91822046 9 0.6121894 0.533333 13 #> 2 A 0.4781883 0.71636154 19 0.6121894 0.533333 13 #> 3 A 0.7647987 0.20624914 15 0.6121894 0.533333 13 #> 4 A 0.7696877 0.81691683 20 0.6121894 0.533333 13 #> 5 A 0.2685485 0.71585943 14 0.6121894 0.533333 13 #> 6 B 0.6730459 0.06062449 14 0.6121894 0.533333 13 #> 7 B 0.9787908 0.84710058 17 0.6121894 0.533333 13 #> 8 B 0.8463270 0.84676044 8 0.6121894 0.533333 13 #> 9 B 0.8566562 0.33261085 16 0.6121894 0.533333 13 #> 10 B 0.4451601 0.55965050 16 0.6121894 0.533333 13 #> 11 C 0.8382325 0.66946933 12 0.6121894 0.533333 13 #> 12 C 0.5833169 0.25463848 18 0.6121894 0.533333 13 #> 13 C 0.5109512 0.07917477 5 0.6121894 0.533333 13 #> 14 C 0.2601681 0.15996809 6 0.6121894 0.533333 13 #> 15 C 0.7494857 0.81639049 6 0.6121894 0.533333 13 r1sd_at(Example, my_vars) #> category x y z s_x s_y s_z #> 1 A 0.1594836 0.91822046 9 -1.8112227 1.22348833 -0.7954674 #> 2 A 0.4781883 0.71636154 19 -0.5361226 0.58181493 1.1932011 #> 3 A 0.7647987 0.20624914 15 0.6105718 -1.03974121 0.3977337 #> 4 A 0.7696877 0.81691683 20 0.6301320 0.90146222 1.3920679 #> 5 A 0.2685485 0.71585943 14 -1.3748670 0.58021879 0.1988668 #> 6 B 0.6730459 0.06062449 14 0.2434797 -1.50265592 0.1988668 #> 7 B 0.9787908 0.84710058 17 1.4667290 0.99741097 0.7954674 #> 8 B 0.8463270 0.84676044 8 0.9367569 0.99632970 -0.9943342 #> 9 B 0.8566562 0.33261085 16 0.9780827 -0.63805992 0.5966005 #> 10 B 0.4451601 0.55965050 16 -0.6682645 0.08365854 0.5966005 #> 11 C 0.8382325 0.66946933 12 0.9043720 0.43275298 -0.1988668 #> 12 C 0.5833169 0.25463848 18 -0.1155155 -0.88592015 0.9943342 #> 13 C 0.5109512 0.07917477 5 -0.4050424 -1.44368791 -1.5909348 #> 14 C 0.2601681 0.15996809 6 -1.4083958 -1.18686040 -1.3920679 #> 15 C 0.7494857 0.81639049 6 0.5493064 0.89978905 -1.3920679 r2sd_at(Example, my_vars) #> category x y z z_x z_y z_z #> 1 A 0.1594836 0.91822046 9 -0.90561135 0.61174417 -0.39773369 #> 2 A 0.4781883 0.71636154 19 -0.26806132 0.29090746 0.59660054 #> 3 A 0.7647987 0.20624914 15 0.30528590 -0.51987061 0.19886685 #> 4 A 0.7696877 0.81691683 20 0.31506602 0.45073111 0.69603396 #> 5 A 0.2685485 0.71585943 14 -0.68743350 0.29010940 0.09943342 #> 6 B 0.6730459 0.06062449 14 0.12173984 -0.75132796 0.09943342 #> 7 B 0.9787908 0.84710058 17 0.73336452 0.49870548 0.39773369 #> 8 B 0.8463270 0.84676044 8 0.46837843 0.49816485 -0.49716712 #> 9 B 0.8566562 0.33261085 16 0.48904135 -0.31902996 0.29830027 #> 10 B 0.4451601 0.55965050 16 -0.33413225 0.04182927 0.29830027 #> 11 C 0.8382325 0.66946933 12 0.45218599 0.21637649 -0.09943342 #> 12 C 0.5833169 0.25463848 18 -0.05775774 -0.44296007 0.49716712 #> 13 C 0.5109512 0.07917477 5 -0.20252121 -0.72184395 -0.79546739 #> 14 C 0.2601681 0.15996809 6 -0.70419791 -0.59343020 -0.69603396 #> 15 C 0.7494857 0.81639049 6 0.27465322 0.44989453 -0.69603396"},{"path":"http://svmiller.com/reference/binred_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"binred_plot() provides diagnostic fit generalized linear model \"binning\" fitted residual values model showing may fall outside 95% error bounds.","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"","code":"binred_plot(model, nbins, plot = TRUE)"},{"path":"http://svmiller.com/reference/binred_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"model fitted GLM model, assuming link \"logit\" nbins number \"bins\" calculation. Defaults rounded square root number observations model absence user-specified override . plot logical, defaults TRUE. TRUE, function plots binned residuals. FALSE, function returns data frame binned residuals.","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"bindred_plot() returns plot ggplot2 object, default. y-axis mean residuals particular bin. x-axis mean fitted values bin. Error bounds 95%. LOESS smoother overlaid solid blue line. plot = FALSE, function returns data frame binned residuals summary whether residuals error bounds.","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"number bins user wants arbitrary. Gelman Hill (2007) say , larger data sets (n >= 100), number bins rounded-square root number observations model. models number observations 10 100, number bins 10. models fewer 10 observations, number bins rounded-number observations (divided 2). default rounded square root number observations model. smart want .","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"","code":"M1 <- glm(vs ~ mpg + cyl + drat, data=mtcars, family=binomial(link=\"logit\")) binred_plot(M1) #> 2 of 6 bins are inside the error bounds. That is approximately 33.33%. An ideal rate is 95%. An acceptable rate is 80%. Any lower than that typically indicates a questionable model fit. Inspect the returned plot for more. #> `geom_smooth()` using method = 'loess' and formula = 'y ~ x' #> Warning: Chernobyl! trL>n 6 #> Warning: Chernobyl! trL>n 6 #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: no non-missing arguments to max; returning -Inf"},{"path":"http://svmiller.com/reference/carrec.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode a Variable — carrec","title":"Recode a Variable — carrec","text":"recodes numeric vector, character vector, factor according fairly simple recode specifications former Stata users appreciate. Yes, taken John Fox's recode() unction car package. going carrec() (.e. shorthand car::recode(), phonetically : \"car-wreck\") package, additional shorthand carr thing. goal minimize number function clashes multiple packages use workflow. example: car, dplyr, Hmisc recode() functions. rely car package just function, conflicts tidyverse functions vital workflow.","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode a Variable — carrec","text":"","code":"carrec(var, recodes, as_fac, as_num = TRUE, levels) carr(...)"},{"path":"http://svmiller.com/reference/carrec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode a Variable — carrec","text":"var numeric vector, character vector, factor recodes character string recode specifications: see , former Stata users find stuff familiar as_fac return factor; default TRUE var factor, FALSE otherwise as_num TRUE (default) .factor FALSE, result coerced numeric values result numeric. want 99% applications regression analysis. levels optional argument specifying order levels returned factor; default use sort order level names. ... optional, make shortcut (carr()) work","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode a Variable — carrec","text":"carrec() returns vector, recoded specifications user. carr() simple shortcut forcarrec().","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Recode a Variable — carrec","text":"Recode specifications appear character string, separated semicolons (see examples ), form input=output. input value satisfies one specification, first (left right) applies. specification satisfied, input value carried result. NA allowed input output.","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Recode a Variable — carrec","text":"Fox, J. Weisberg, S. (2019). R Companion Applied Regression, Third Edition, Sage.","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Recode a Variable — carrec","text":"John Fox","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode a Variable — carrec","text":"","code":"x <- seq(1,10) carrec(x,\"0=0;1:2=1;3:5=2;6:10=3\") #> [1] 1 1 2 2 2 3 3 3 3 3"},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Charitable Contributions Panel Data — charitable_contributions","title":"Charitable Contributions Panel Data — charitable_contributions","text":"toy panel data set charitable contributions across 10 years 47 taxpayers. useful illustrating estimation panel models.","code":""},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Charitable Contributions Panel Data — charitable_contributions","text":"","code":"charitable_contributions"},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Charitable Contributions Panel Data — charitable_contributions","text":"data frame 470 observations following 8 variables. subject numeric identifier subject time numeric time identifier, simple integer 1 10 charity sum cash property contributions, excluding carry-overs previous years income adjusted gross income price 1 minus marginal income tax rate, defined income prior contributions age dummy variable equals 1 respondent 64, 0 otherwise ms dummy variable equals 1 respondent married, 0 otherwise deps number claimed dependents, integer","code":""},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Charitable Contributions Panel Data — charitable_contributions","text":"Frees (2003) nominal source data, appear toy data sets use book. turn cites Banerjee Frees (1995), though citation may meant 1997 article Journal American Statistical Association. actual source data obtained Gujarati (2012). underlying source raw data supposedly 1979-1988 Statistics Income Panel Individual Tax Returns. Given opacity data, temporal limitations, data used illustration inference. charitable contributions variable income variables clearly log-transformed. Banerjee Price (1997) seem imply price variable well.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate Data from Correlation Matrix — cor2data","title":"Simulate Data from Correlation Matrix — cor2data","text":"function simulate data correlation matrix. useful illustrating theoretical properties regressions population parameters known set advance.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate Data from Correlation Matrix — cor2data","text":"","code":"cor2data(cor, n, seed)"},{"path":"http://svmiller.com/reference/cor2data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate Data from Correlation Matrix — cor2data","text":"cor correlation matrix (class matrix) n number observations simulate seed optional parameter set seed. Omitting generates new simulations every time.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate Data from Correlation Matrix — cor2data","text":"cor2data() returns data frame observations simulated standard normal distribution, pre-set correlations.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate Data from Correlation Matrix — cor2data","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate Data from Correlation Matrix — cor2data","text":"","code":"vars <- c(\"control\", \"treat\", \"instr\", \"e\") Correlations <- matrix(cbind(1, 0.001, 0.001, 0.001, 0.001, 1, 0.85, -0.5, 0.001, 0.85, 1, 0.001, 0.001, -0.5, 0.001, 1),nrow=4) rownames(Correlations) <- colnames(Correlations) <- vars cor2data(Correlations, 1000, 8675309) #> control treat instr e #> 1 -0.9965823534 0.7208272032 0.287418622 -0.2321504442 #> 2 1.0654160534 0.9882846616 0.854663879 -0.2618288603 #> 3 0.5720665025 0.9042493136 -0.047579748 -1.3874597782 #> 4 0.1500831690 -0.6598135468 -1.084751166 0.1370684857 #> 5 -0.4418016435 -0.9010785615 -0.845310165 0.0715451179 #> 6 1.9858258232 0.0459908373 -0.173578835 -0.4935294263 #> 7 -0.4148232405 0.6828190627 0.943919855 0.3814435259 #> 8 -0.1861047636 0.3827595299 0.523484928 0.4700195250 #> 9 1.5749027754 0.5901018972 0.177827618 -0.8610024992 #> 10 0.0639170359 -0.3134096367 -0.397293735 -0.0918152377 #> 11 -0.6352569781 -0.0044295617 0.020168913 0.2501361307 #> 12 -0.2520425211 -0.1512905007 -0.589533425 -1.2448928940 #> 13 0.2376632053 0.0363285505 0.101976860 0.1277191406 #> 14 -0.8255891282 -2.1360607862 -1.702933848 0.9465364294 #> 15 -0.6516404496 0.7280512605 1.171750633 0.5155311475 #> 16 1.5969914054 0.5656807418 0.399226237 -0.7703476721 #> 17 -0.1421471777 0.9006403891 0.954204281 -0.4455227784 #> 18 -1.3220235033 0.2264778728 0.009064416 0.0065538113 #> 19 -0.2246474073 -2.5970891486 -2.693111816 1.0079821345 #> 20 -0.9394608113 -0.8518025099 0.150778171 1.3469793319 #> 21 0.0860048804 1.2516636414 1.385327986 -0.1259933303 #> 22 0.7225398120 -1.1077042306 -0.535931502 1.4957405636 #> 23 0.0729270191 -1.3144007994 -1.014642021 0.9981142810 #> 24 -0.9260292594 -0.7883291769 -0.361114780 1.0344957861 #> 25 1.0869857446 0.1321047285 -0.094669783 0.2438068996 #> 26 0.7445376777 -1.3458827971 -0.969975143 0.9370971713 #> 27 -0.4636759603 0.2044783119 0.418151000 0.0377759209 #> 28 -1.0655403666 -0.2639171355 -1.106705577 -1.1383977098 #> 29 -0.5287039864 1.1551101158 1.358809253 0.1315152717 #> 30 -0.2198145126 1.6884225118 1.376292202 -1.2509852977 #> 31 1.4954756842 -0.6883876150 0.141941256 1.1002280342 #> 32 0.3025106599 0.4001815241 0.337654874 0.1698237556 #> 33 0.5959807404 -0.4945399548 -0.073658970 1.0189431924 #> 34 -0.7104127738 -0.7060516974 -0.143037533 0.9064538362 #> 35 -0.0514446168 0.0376181078 -0.004882204 -0.2263715972 #> 36 -0.6982394936 -1.6994442420 -0.922406514 1.3535014727 #> 37 -0.5044070188 -0.1559585619 0.483247583 1.1880071951 #> 38 1.3183341418 0.2652751179 0.057576689 -0.1052884727 #> 39 1.4227405795 -1.0976779308 -1.251364754 -0.3184538038 #> 40 -2.0903851451 -0.9361926111 -0.640157926 0.5808296039 #> 41 0.9157140623 0.7301538554 0.389022933 -0.8004601103 #> 42 -2.5986449276 -1.2471576837 -1.605230246 -0.0884306389 #> 43 -1.0827933118 1.0084724666 -0.255951690 -2.0251737305 #> 44 0.7107506137 0.2133284164 0.967608811 1.2282214916 #> 45 -0.5455656264 0.4292847814 -0.115005861 -1.2679471874 #> 46 1.9146288126 1.4422300806 1.313794767 -1.0812488577 #> 47 -0.0677403499 0.0014178806 -1.310050717 -2.2700781341 #> 48 -1.9030759641 0.9880112464 0.965876945 -0.8509524730 #> 49 0.1457032985 0.4464379034 0.691172211 0.5672443530 #> 50 0.8362996636 0.4565637332 0.684055018 0.6873292995 #> 51 -0.0883647604 0.0810519541 0.800131749 1.0746550525 #> 52 0.9875926895 -0.1437086878 -0.439892046 -0.4734800696 #> 53 0.8726287585 -1.2015007831 -0.912452889 0.6801547630 #> 54 2.8694916820 -1.2024765360 -1.235091185 0.3560542533 #> 55 2.1171789017 -0.2789195895 0.103711855 -0.0173371770 #> 56 0.3871995746 1.0305680297 1.148117138 -0.0667032728 #> 57 -0.0726821124 -0.0138292124 -1.446099596 -2.0674425463 #> 58 1.2316246793 1.5915372558 1.084209644 -0.7922543166 #> 59 -0.8905042661 -1.5542798939 -0.598894451 1.6960736036 #> 60 0.0548748434 -1.1205033546 -0.204322826 2.0539886074 #> 61 0.4856123438 -0.0738361454 -0.447533352 -0.2342989632 #> 62 -1.6609907926 -0.9196301146 -1.504469271 -0.3646811626 #> 63 -0.7475356712 -1.0299982665 -1.050883913 0.1909882843 #> 64 -0.1874381301 1.1561341740 0.488461352 -1.1746531529 #> 65 -0.1280866986 -1.8264393518 -1.271893286 1.3563195366 #> 66 1.0161407701 0.2732879917 0.759082747 1.1700572167 #> 67 -0.0295302106 -0.3974142596 -0.388279281 -0.1360664977 #> 68 0.6139023858 0.0600806651 -0.832884040 -1.2413581354 #> 69 -1.8605476217 0.8611896280 0.208222326 -1.3874016015 #> 70 0.5176822404 -0.3542435118 -0.402401985 0.6143337740 #> 71 1.8119469018 0.3284199945 0.263171240 -0.0842571110 #> 72 -0.7192566870 -0.1676594684 -1.401654429 -2.0711198450 #> 73 -0.4689301875 0.2178897637 -0.070228936 0.0174193448 #> 74 -0.1271406030 -1.0523956093 -0.511933181 0.8729266243 #> 75 -1.1372336125 1.0122075493 0.749032578 -0.7273827565 #> 76 0.2649755539 1.0364260330 0.603182721 -0.9363068185 #> 77 0.2215458135 1.0018328125 1.532088895 0.8469385896 #> 78 -0.2418763513 -0.0026224299 0.317268293 0.6142776491 #> 79 0.1914502549 -0.2086116513 0.181531556 0.5119477399 #> 80 1.8800048735 -0.3239213317 -0.820185321 -1.0391501073 #> 81 0.0082917758 -0.0432110486 -0.306725231 -0.6065549685 #> 82 0.6667589254 -2.4044473421 -1.667889940 1.6771065500 #> 83 0.9175493351 0.7405620424 0.847903202 0.0814382458 #> 84 -0.6274929339 -1.0379196426 -0.937744507 0.5679629185 #> 85 -2.3842849277 -1.0758225239 -1.593280834 -0.5959578697 #> 86 -2.1428924005 -0.4764390312 -1.056403131 -0.5356201190 #> 87 0.2287578980 -0.2345622485 -0.422246655 -0.3665414560 #> 88 0.0536125533 0.0995781237 0.625111473 0.6583542295 #> 89 1.2267928812 0.5508514231 0.679968304 -0.0940431842 #> 90 0.4780816209 0.3275146299 -0.371817907 -1.5343363202 #> 91 0.3390401636 -1.2040982561 -2.397138525 -0.9272155705 #> 92 -0.0076861861 -0.5841703177 0.185066391 1.0579120807 #> 93 1.5291182883 0.8415548048 0.415501082 -0.8642873881 #> 94 0.4121599446 0.4340511225 0.092346062 -0.5901927536 #> 95 -0.8235687345 -0.8165891495 -0.567970387 -0.4023676516 #> 96 0.4954646491 -0.0002349125 0.387900028 1.0438416893 #> 97 0.1266491400 -0.6790643671 0.043058380 1.3790941535 #> 98 -0.5742761291 1.5984695389 0.704856808 -2.3542412644 #> 99 1.0010295847 0.2412304459 -0.384928717 -1.5592964356 #> 100 -1.1497760224 -1.2753716389 -1.142056448 0.2193758575 #> 101 0.1492778982 -0.1878344589 0.278414430 0.4367586205 #> 102 1.3997262697 0.4133508578 -0.051626586 -0.5362754147 #> 103 -1.0861982967 0.0700424135 -0.106325586 -0.2547302592 #> 104 -1.7474647786 -0.1227033807 -0.012243466 0.1094999363 #> 105 1.4272417329 -0.7406452097 -0.581810418 0.4674830928 #> 106 -0.9862928998 -0.1929405800 1.167368135 1.6394755966 #> 107 1.2863867167 -0.9951159830 -1.012189573 0.4951382816 #> 108 -0.4154368119 0.2572890197 -0.014111532 -1.0238290290 #> 109 0.2383302354 2.0102969808 2.239901452 -0.3614790130 #> 110 -0.7402096283 -1.2716446692 -1.510427813 -0.4514989512 #> 111 2.0466815674 -0.4928636163 -0.604210230 0.3055909873 #> 112 -2.0885948386 -0.3708611734 -0.373955669 -0.0174903503 #> 113 -0.5253580619 0.4811786721 0.948507514 0.4269440303 #> 114 -1.3154965328 -0.3952516303 -0.278402001 0.0192981349 #> 115 -0.0303048245 0.2226108103 0.034709658 -0.4323617290 #> 116 0.5124912363 -0.5625252783 -1.492788514 -1.2750845973 #> 117 1.4027150278 -1.8468250323 -2.699885004 -1.3446254759 #> 118 -0.6843599054 -1.0991092799 -1.641203736 0.1529525415 #> 119 1.8252950496 -1.4469577484 -0.496995199 1.9405547298 #> 120 -0.0510802981 3.4127416222 2.966804373 -1.7116465498 #> 121 -0.7758053871 -0.2263915757 0.022063956 0.6432251810 #> 122 -0.0005582601 0.3585390693 0.634236312 0.7141508302 #> 123 0.9439207858 -0.7058713840 -0.629653884 0.4224257279 #> 124 -0.3935517312 -1.4327522470 -1.563415818 -0.1711132676 #> 125 1.1514813248 -0.2884294070 0.857540507 1.0142849980 #> 126 -0.3912258906 2.2123300336 1.057275663 -2.6091010052 #> 127 -0.5547058433 1.4099466118 0.940140437 -1.0799053201 #> 128 0.3878424248 -0.9867081529 -2.302795191 -1.5966073234 #> 129 1.7572682273 -0.4069311421 -0.322776824 0.6044065326 #> 130 0.2621760480 2.1378381939 2.485834233 -0.1112496036 #> 131 1.5285383011 0.4537084767 1.280318580 1.4632770031 #> 132 1.0636595441 0.7214983716 1.792670070 1.5226734410 #> 133 0.2661299867 0.3060146691 -1.180210087 -2.5028086036 #> 134 0.3898872330 -1.0414902417 -1.504870734 0.5376234616 #> 135 0.2823965234 0.4257273140 1.026864538 0.7606334073 #> 136 -1.0757606536 -1.4961322802 -1.450717577 0.6128116223 #> 137 -0.4716045915 -1.3673918777 -1.395710451 0.4436689822 #> 138 -0.4424640222 1.6483200693 2.212215349 0.5527104635 #> 139 -1.0262684941 -0.6913329273 -1.090292090 -0.9713300623 #> 140 0.7781756063 -0.0582186721 0.322760231 0.5025193014 #> 141 -0.8269995163 -0.1629937838 -0.131652145 0.4391648907 #> 142 0.7248825578 0.3828298091 0.234452233 -0.6325338227 #> 143 0.4571013558 -0.5978666319 -1.335766528 -0.7418804781 #> 144 -0.0802096753 -0.5319872317 -0.731627000 0.3842356647 #> 145 -2.2309976562 0.3083798619 -0.388946280 -1.2305794313 #> 146 0.9363910393 0.5448630454 0.922837506 0.1709309695 #> 147 -1.6663139173 0.8273801456 0.234860508 -0.9234716121 #> 148 -0.3158055757 -0.4872280447 0.136331022 1.1068604597 #> 149 1.1365227782 -1.0124328991 -1.187312042 -0.4356236313 #> 150 -0.6525849499 -0.3927670669 0.115626843 0.8535295583 #> 151 -0.9528813410 0.7777425800 0.580039919 -0.5775641155 #> 152 0.1131788577 -0.5663035470 0.864162929 2.1379974913 #> 153 0.3533720065 -1.3588584424 -1.158939547 -0.0021350440 #> 154 -0.5102808937 -1.7623467969 -1.483675925 0.8149103795 #> 155 -1.1867309068 0.1023429468 0.558628161 0.1671132169 #> 156 0.3666060039 -0.5590729597 -1.215733242 -0.9240624420 #> 157 2.4966007633 0.9940866361 0.252677123 -1.1833951997 #> 158 -0.9915848454 0.5353980635 -0.490589215 -1.7897328622 #> 159 -1.8849580713 1.6450591620 0.674987814 -2.3225385919 #> 160 0.1687427678 0.5417204857 0.257332509 -0.7896209422 #> 161 0.2091340054 -0.2354308223 -0.798440022 -0.6502562592 #> 162 0.5175410155 -0.0267999731 0.620030078 1.0501397413 #> 163 0.0909253216 -1.1861863609 -0.923506257 0.5622934454 #> 164 -2.2255201755 -0.1455957333 -0.639005185 -0.8918414268 #> 165 1.2820031223 0.8039236999 0.913827074 -0.0737098914 #> 166 -0.4358379868 1.0734230168 1.273311743 -0.2100532587 #> 167 1.2532314902 -0.5291994279 0.534139063 2.1869417346 #> 168 1.0223172006 0.5687467504 0.903411187 0.6261027703 #> 169 -0.5756838860 0.5964307130 1.015942880 0.4251927752 #> 170 -1.0986413625 -0.5056961803 -0.661587863 -0.0538951952 #> 171 -1.3897765802 -0.0626112008 0.160780005 0.3167848300 #> 172 0.0746746899 -1.2073097185 -1.459447439 -0.3336329100 #> 173 1.2060502336 0.1292531669 -0.382794857 -0.9884968772 #> 174 1.7170201266 0.3877822582 0.857906381 -0.1455073027 #> 175 0.9365307877 1.7577995665 2.390797144 0.3068649257 #> 176 -2.0417007436 -0.0261505651 -0.282652293 0.1079130368 #> 177 -0.2249723988 -0.9723234811 -1.488516843 -0.3782198015 #> 178 -1.2326383202 0.8761481986 1.156920639 0.5704553731 #> 179 0.4374933990 -0.6448234862 0.104012080 1.6918202360 #> 180 -0.8353385222 -0.3594569680 -0.678154444 0.2805590170 #> 181 -2.4682653911 0.4529616387 1.447409424 1.6059427309 #> 182 -0.8782746883 -0.9263071546 -0.568454195 0.5820833909 #> 183 2.0642082674 0.3918494391 0.608336379 0.6974516233 #> 184 -0.3623272868 0.5337175388 -0.241122803 -0.7156792161 #> 185 1.1992259260 0.5712763986 -0.018610264 -0.8216855895 #> 186 1.1566529876 2.3059498265 2.204193445 -0.6905793552 #> 187 -0.9467408667 0.8379282653 1.074336481 0.3709542444 #> 188 0.4216108419 -0.0775368945 0.004224884 0.5130498202 #> 189 -0.0213208768 0.3631307413 -0.533567742 -1.1107489581 #> 190 0.3691331549 -0.4352823774 -0.859039979 -0.6691177792 #> 191 0.7333818988 0.2156083596 -0.805457083 -1.6371378522 #> 192 -0.8328036383 0.6845159841 -0.253614857 -1.2719939835 #> 193 1.0290869320 0.4934103112 -0.381879147 -1.4001220145 #> 194 -0.0182059685 1.6461686776 0.770662096 -1.7830646353 #> 195 0.4414848629 0.1905353362 0.345400964 0.2168995830 #> 196 -1.0306940473 0.9846023302 0.954199845 -0.6928303889 #> 197 -1.0337461899 0.1133996422 -0.496983518 -1.2554102559 #> 198 -1.0482805159 0.9973878149 -0.159234931 -2.3523600989 #> 199 -0.5617622806 -1.7970369104 -0.648694172 2.3977650898 #> 200 -0.4923549495 0.3514079443 -0.098047038 -0.7165782768 #> 201 1.3438289133 -0.9305745716 -1.003301724 -0.0390457188 #> 202 2.1347890594 0.3716704697 0.220537935 -0.7268333842 #> 203 0.5029592102 0.2448305583 0.229626686 -0.2575460846 #> 204 -0.9016388060 -1.5795719936 -0.717380246 1.9531262993 #> 205 -0.4263660697 0.6307585589 -0.046904405 -0.8417062628 #> 206 0.3115565821 -1.2866067342 -1.023282417 0.8695940757 #> 207 0.2988763148 -0.9745552518 -1.383254275 -0.5947413117 #> 208 1.2361565709 1.4150352851 1.031871616 -0.8904936580 #> 209 0.3725077679 0.7942484751 0.132774271 -0.7838227572 #> 210 -1.1750773417 0.5637584562 0.851171556 0.3130932654 #> 211 0.4965627441 0.2859975820 -0.293047763 -0.5899249310 #> 212 0.9325149534 1.4457924185 1.743031573 0.5754142256 #> 213 -0.7193845744 -1.1378050408 -0.577649370 0.9627493326 #> 214 -0.6920256098 -1.4194196331 -1.203043268 1.2643590826 #> 215 -2.0708013532 -0.6500758304 -0.023345928 1.2083537898 #> 216 -1.1885767601 -0.5017115532 -0.271143675 -0.5812068578 #> 217 1.0196957278 -0.8610112878 -0.551093910 0.1291345517 #> 218 -0.9801600387 -0.2082992619 0.389801964 0.8730471608 #> 219 -0.3048293564 0.7502682700 1.029307885 0.5022445041 #> 220 0.3891123079 -1.4515286003 -0.341255446 2.0239422442 #> 221 0.0480109920 0.1224140864 0.892263451 1.4284697193 #> 222 -0.7807094420 0.8781411585 0.973867116 -0.5928480434 #> 223 0.3739462760 -0.8071012499 -0.693135292 0.3111877553 #> 224 -0.5206896437 -1.7094542626 -0.948415367 2.3886446295 #> 225 1.3589583509 -0.9365137448 -0.368246157 1.5254623275 #> 226 -0.1902957880 0.5583270618 0.944778218 0.8145860387 #> 227 0.8426863822 1.2789201420 0.648705397 -1.4287494782 #> 228 0.7486460396 0.3554323220 -0.028549604 -0.8517924820 #> 229 0.3735456722 1.4573323841 1.318270463 -0.3206674503 #> 230 -0.7044303714 -0.4210005692 -0.122780414 0.6560503234 #> 231 -0.3151017812 -2.5245049596 -3.216464771 -0.2625191561 #> 232 -0.1678472734 -0.1113213979 -0.690901705 -1.1625406938 #> 233 -0.4443966995 0.6058941835 -0.166939722 -1.1096497136 #> 234 -1.4453786361 -0.7761605000 -1.031998488 -0.9941266056 #> 235 0.0107456987 0.2661966717 -0.391321737 -1.0043260461 #> 236 2.3317732785 -0.0823524628 0.021934832 0.2577879525 #> 237 0.1533153891 1.8133084612 1.852670561 -0.6178718603 #> 238 -1.7078257765 0.5809316555 1.379390135 0.2923790416 #> 239 0.0255381778 0.0126695856 -0.641503092 -1.1074941365 #> 240 -0.8791096861 0.6689311324 -0.388580194 -1.9334251873 #> 241 0.9540100928 -0.2360637047 -0.993013280 -1.3823952457 #> 242 -0.0180071628 -0.4643724382 -0.619287402 -0.2720526855 #> 243 -0.0712532550 -0.1695863082 -0.040577592 0.6303829396 #> 244 0.4990261682 0.1983377790 0.985607987 1.2279198734 #> 245 -0.3182756389 -0.9861697977 -0.871669247 0.5164891305 #> 246 0.3689123669 2.2379268380 2.038814831 -1.0210297753 #> 247 -0.7540141567 0.0360802881 0.876243245 1.1994393013 #> 248 -1.7738158349 -0.3962555216 -0.931874283 -1.2129961044 #> 249 0.9244368621 -1.3796205570 -0.629627354 1.7208066084 #> 250 1.1331602020 0.2442510501 -0.336933015 -1.1373761986 #> 251 -0.2674132575 -1.2634592822 -1.690355198 -0.0975583499 #> 252 1.6318222029 -0.2327109961 0.144170269 0.9408143231 #> 253 0.1051333094 0.1612631385 0.464902466 0.3053051776 #> 254 -1.3313872948 0.4060363766 -0.073564495 -0.7883624695 #> 255 -0.1029043378 0.5271073142 0.464145277 -0.0168736887 #> 256 -1.0501151829 1.4205127409 1.587905671 0.3326375027 #> 257 1.5842152883 -0.9701860555 -1.143126556 0.6259898261 #> 258 1.2641116408 -0.5373803362 -0.306766416 0.6633805100 #> 259 -1.1991583710 0.9388782635 1.165766248 0.1458027912 #> 260 -0.5839066312 -1.6538989171 -1.011080747 1.1684412153 #> 261 1.5844734828 0.4286997540 -0.238708420 -0.8713216856 #> 262 -2.6945196593 -0.4815923633 -0.535670337 -0.0017371586 #> 263 0.5454795327 0.0418462623 -0.549961196 -0.5534608490 #> 264 -0.9506309332 0.1965712386 0.443794509 0.1765411871 #> 265 0.7514034416 0.2406764140 0.579317408 0.5815578804 #> 266 0.6142840582 0.8469276132 0.394999941 -0.6755548287 #> 267 -0.4919001423 -1.1048747964 -0.679254326 0.3342529144 #> 268 1.0143629916 -0.6647525413 -1.182006730 -0.7184851595 #> 269 -0.3153580379 -0.2381459381 -0.705346260 -0.6934713197 #> 270 0.6325127572 0.6132209060 0.030091989 -1.0159158140 #> 271 0.8749485655 1.7581064136 1.456690652 -0.7531915512 #> 272 -0.8460066431 0.8281121824 1.068446301 -0.3335038297 #> 273 0.5448628782 0.9754610260 0.658936208 -1.2118555876 #> 274 0.4665643847 -0.8202513758 0.175764319 1.5790956599 #> 275 -1.5169966854 -0.8295641193 -0.313422978 0.6962046037 #> 276 0.9876591908 -0.8968688473 -1.777296315 -1.1713239772 #> 277 0.1293742393 1.1218232146 1.565713286 0.6381700248 #> 278 0.3752447946 0.4804923916 -0.274679522 -0.9758553360 #> 279 -0.0238203074 0.1374159843 0.378496456 0.4855705315 #> 280 -1.6291997286 0.2742964141 0.244684310 -0.7070943938 #> 281 -1.6699517714 1.1292589485 0.933862913 -0.6966269628 #> 282 0.3636184086 0.7519561838 1.212753893 0.1175917910 #> 283 2.1158338281 -0.1591687702 0.573899370 0.8984556679 #> 284 0.4324093988 -1.8064270319 -0.991296215 2.2448631295 #> 285 -0.3625831851 -0.7226585150 -0.745151834 0.5812348801 #> 286 -0.6285519008 0.5434036571 0.451718361 -0.2651429282 #> 287 0.9612526587 1.0020570444 1.307535982 -0.1084146433 #> 288 0.7018444191 0.5197885720 0.374029934 -0.1622017582 #> 289 -0.1901001582 -0.5561041378 -0.806349033 0.5274962196 #> 290 0.1829207990 -0.3390565172 -0.181038984 0.2496733341 #> 291 0.9286072852 0.3951947465 0.147856177 -0.8698139627 #> 292 0.2494420210 0.2550107506 -0.188711569 -0.4452841919 #> 293 -0.2398222790 0.5349858279 0.707468393 0.7915199311 #> 294 -1.6477069868 0.6508774297 0.615926241 -0.3869756141 #> 295 -1.1604467232 -0.0360506117 1.220157923 2.3192568643 #> 296 1.6752822367 0.2648159369 -0.056379112 -0.5357413910 #> 297 1.3916675385 0.5873993404 0.048830227 -1.8262191834 #> 298 -0.1026699377 -2.5441943458 -1.123517310 2.6627810984 #> 299 -0.9011659158 -0.5907666882 -0.712635239 -0.4363938110 #> 300 0.1009469272 -0.2512645456 -0.428644169 -0.2196907328 #> 301 0.5701397198 0.6229924238 -0.411861891 -1.4752699656 #> 302 -0.4913474370 2.5589252171 2.109169293 -1.5118205257 #> 303 -0.2740915671 1.6067836304 1.320835189 -0.8019768852 #> 304 -0.1493990078 -1.1940669112 -0.601723162 1.0104986959 #> 305 0.1806294429 0.3618992058 -0.426356061 -1.2284893588 #> 306 0.9203113540 0.8471292841 0.976520145 -0.0505772592 #> 307 0.6363767277 -1.1198105112 -1.651323126 -0.5850667997 #> 308 -0.0717002434 -1.4458888461 -1.432950866 0.9059859784 #> 309 0.1417651321 -0.9765072644 -1.717407807 -0.3215750427 #> 310 0.0765428275 0.3714029420 0.454733385 -0.3511896609 #> 311 -0.5125867486 0.9225335720 1.071650268 -0.0639684412 #> 312 0.4592486300 -1.9371112036 -0.947501824 2.9330613046 #> 313 0.7145027507 -0.0743239280 -0.277175214 -0.4048250364 #> 314 -1.5428035777 -0.2163269309 -0.251079553 -0.2636734044 #> 315 0.2061877846 0.6356931922 0.261243813 -0.7846487128 #> 316 -1.1616292189 -0.0515683838 0.029608557 -0.1323032707 #> 317 -1.7157991732 0.6936224222 0.131115662 -1.3314650774 #> 318 1.0519574733 1.9078787312 2.073076887 -0.1455859829 #> 319 1.0116796542 -0.6329067385 -0.671120203 0.1189316110 #> 320 0.1847126789 0.4380002345 -0.875706407 -1.6711166980 #> 321 1.6767425787 0.5010694130 -0.456372462 -2.0617870776 #> 322 -0.4534593889 1.5767933183 1.083570461 -1.6729442999 #> 323 0.7883146965 1.4165491436 0.809588321 -1.5257566939 #> 324 -1.0750273253 0.2460655546 0.101374746 -0.1026141631 #> 325 -1.4312032525 1.7954515293 1.594907950 -0.4779589391 #> 326 -1.4320176152 0.6985054259 -0.035443174 -1.5690386149 #> 327 0.7132062860 -0.4226373411 -0.640128070 -0.1751268305 #> 328 -0.4070435818 2.0118748925 1.187285069 -1.7067452023 #> 329 0.8955826542 0.6570106790 0.616039350 -0.2185563354 #> 330 1.0609209825 0.4140871392 -0.151646120 -0.8269233665 #> 331 -0.1283071285 -0.2056209458 -0.527976391 -0.2152962214 #> 332 -0.3534121427 -0.1714811880 -0.595512425 -0.6705048718 #> 333 0.3905649031 1.9834950801 2.331166768 -0.1366979754 #> 334 -1.4406009960 0.4863349765 0.359713075 -0.0636714628 #> 335 0.2101222473 -0.0369538087 0.003392888 0.3434410124 #> 336 -0.1960052650 0.8302718114 1.305331937 0.3763809018 #> 337 1.6459129661 2.4942973841 2.961324135 0.0324389514 #> 338 0.7417086124 -0.5328064896 0.440451601 1.5282767958 #> 339 1.4171752132 0.3031856504 1.159765491 1.7817615015 #> 340 0.1618826881 -1.5363450575 -0.667576964 1.3800910026 #> 341 1.0530199137 -0.7880203428 -0.354450239 1.2466205734 #> 342 -0.8629300826 -0.6170369925 -0.567648774 0.4307236522 #> 343 -0.9876900868 0.2142686499 -0.486533035 -1.2589039968 #> 344 -0.1765111396 0.7279805859 -0.049142366 -1.5647595850 #> 345 1.4989045992 -1.8134003480 -1.709435893 0.1217705949 #> 346 -0.3872961187 -1.1599387007 -1.036779106 0.6755510838 #> 347 -0.2844547099 0.3509265127 -0.520787961 -1.2609036056 #> 348 0.7319427364 -0.5004616837 0.284351916 1.7849449723 #> 349 -0.1916819763 -0.4810719573 -0.733740352 -0.0415153921 #> 350 0.8569219797 0.0347175230 0.122578553 0.3911469185 #> 351 -0.6528180628 0.6176099289 1.522255162 1.4603993223 #> 352 0.8444521334 1.5665165850 1.768368897 0.3728710132 #> 353 0.1937976228 -0.3287726153 -0.500388400 0.2252237539 #> 354 3.5047769131 -0.8626272391 -0.048541472 1.6422252584 #> 355 1.2304051311 -0.7528533563 -0.615962290 0.2766748568 #> 356 1.2874595865 0.4893891858 0.113566248 -0.1678104008 #> 357 -1.6676795461 -0.7063969362 -0.091373772 1.2031327289 #> 358 -0.0730305844 -1.5461599430 -1.814237778 0.5295763521 #> 359 0.3660375713 -0.7516096867 0.818604647 2.3462634762 #> 360 -0.4034299422 -1.4190953257 -1.661267452 0.0624119982 #> 361 -0.6209244841 0.3658087704 0.659931242 0.3250339496 #> 362 -0.4087582719 -0.3829469582 0.173965738 1.0231314989 #> 363 -0.6853750355 -0.4838779967 -1.037893590 -0.5621468821 #> 364 -1.1746949951 -0.6560346275 -0.765654875 -0.2937199527 #> 365 0.0243395012 -0.1221776746 -0.318842198 -0.6656854212 #> 366 1.7476997203 0.8977599137 0.531026617 -0.3695171302 #> 367 -0.9797732090 0.2838708503 -0.665431677 -2.0005838538 #> 368 -0.1552940467 -0.2155429835 -0.209894612 -0.1556285159 #> 369 0.7056420115 0.3581111943 0.794260947 0.5860510366 #> 370 -0.8719316004 1.5856655382 2.001702058 0.1163616723 #> 371 1.8819858033 0.0694265719 -0.984684377 -1.2980457299 #> 372 2.7639737951 0.5580330448 1.263781206 0.9627958471 #> 373 0.1525976701 0.3864863275 0.023111239 -0.7168214841 #> 374 0.4356820263 0.9880301825 1.099954318 -0.2082770965 #> 375 -0.6491587611 -1.3918886630 -1.713625527 -0.4386774295 #> 376 0.6821681711 0.7439039785 1.031912434 -0.1573269000 #> 377 -1.0079860200 -1.9077195878 -1.131081010 2.3369610917 #> 378 -0.5158933810 -1.1859040237 -2.021058030 -0.8421650458 #> 379 0.1399412287 0.1941451493 -0.675330140 -1.6568894745 #> 380 0.5022471741 0.0512675518 0.497222646 0.3764745375 #> 381 0.3455305280 0.2720032247 1.096770120 1.7558703391 #> 382 -1.2599196267 1.9275649222 0.790693848 -1.8365292004 #> 383 2.0546519667 -0.8649065014 -0.019558686 1.5142767611 #> 384 -1.3087259840 0.8783222501 0.680309390 -0.4148925084 #> 385 1.2606879261 1.8677012482 1.035748203 -1.7954234500 #> 386 -0.7347787143 -0.4010886457 -0.803652529 -0.7055352059 #> 387 -0.8432939071 -0.6918389552 -0.341437331 0.5208920877 #> 388 0.3567822204 0.5441837150 0.214405678 -0.8707356167 #> 389 0.2995354832 -0.6686016983 -0.776746060 -0.1017740490 #> 390 -0.9658036496 0.0780165771 0.601682103 1.0329565320 #> 391 0.1072444616 0.0337390463 0.559038450 0.9212212674 #> 392 -0.8176100869 -1.7965263695 -1.549527885 0.5502598651 #> 393 -0.3508041173 1.6793515928 1.201625854 -1.0342441839 #> 394 0.7157549184 0.9789819107 0.287374468 -1.6216453265 #> 395 -1.7987508315 1.1070478197 1.020053987 -0.1828728483 #> 396 0.6221782252 -0.6611676555 -0.331243850 0.4988969582 #> 397 0.1174344264 3.3352403176 2.666975376 -1.6115548384 #> 398 1.5395038369 -2.3561705634 -1.139170219 1.7928743752 #> 399 -2.2636579988 0.0323317977 -0.473795529 -0.4402924935 #> 400 0.5412221902 -0.1134194763 0.020237679 0.8065687922 #> 401 -0.3883670265 0.2066031424 -0.013513493 -0.6038911526 #> 402 1.1805162024 -1.9626380877 -1.769164440 0.7463525440 #> 403 -1.2090300199 -1.0515255127 -1.880900508 -0.8593334869 #> 404 0.0751682157 -0.4169381547 -0.858112025 -0.6402859630 #> 405 -0.7306621351 1.0826530380 1.616268694 0.4389279579 #> 406 1.2617190562 -1.4385945367 -0.554434874 1.5789736000 #> 407 -0.9157153728 0.6563302076 0.529551129 0.1258161329 #> 408 -1.0938949178 -1.4318848990 -1.386201461 0.7953393148 #> 409 -0.2091014900 -0.2585955465 -0.837508710 -0.5112342547 #> 410 0.6991478407 -1.9312236820 -2.071420127 0.7139383502 #> 411 1.8323377590 -0.3377152571 -0.511234582 -0.5473310492 #> 412 1.1989168116 1.7912426612 2.427893021 -0.1472047951 #> 413 0.1132024951 0.2783492412 0.127021569 -0.1659268428 #> 414 -1.6113347125 0.5693820410 -0.208477322 -1.5175355325 #> 415 0.3939993845 -0.1246163708 -0.390615445 -0.5688216603 #> 416 -0.1497863962 2.4769721725 2.042417579 -1.1125542657 #> 417 -1.8690536497 -1.9470285568 -1.937182029 0.6132399410 #> 418 -1.0673105099 -0.6330693498 -0.164061919 0.8946473126 #> 419 -0.0210945084 -0.6511790266 0.110684473 1.0962611272 #> 420 -0.1371906850 -0.6563974548 -1.091420044 -0.6866680373 #> 421 1.6004581583 1.6354727668 1.481626336 -0.5908770201 #> 422 -0.2406808938 0.1501999293 0.027759089 -0.0840291248 #> 423 -0.1673739516 0.9753319019 1.460596125 0.1860894328 #> 424 -1.9605639591 -1.4554562550 -1.963552655 -0.5324245422 #> 425 -1.8590150046 0.1295363724 0.619646214 0.4816644727 #> 426 -0.4903491998 -1.3143105886 -0.415370462 2.1002882642 #> 427 1.2959768232 0.8198351128 0.959415808 -0.8609090413 #> 428 0.3227093984 -1.1299253801 -1.487175634 0.2031890243 #> 429 -0.5781547884 1.6876257599 2.174394740 0.1301641235 #> 430 1.3035753661 0.6790823388 1.969242879 1.5984857543 #> 431 1.1240110132 -0.6406744664 -0.783901680 -0.3486267810 #> 432 -0.5612869711 0.7083478457 1.094187526 -0.0173927132 #> 433 1.1457205744 -0.0179219044 -0.367075450 -0.3710182244 #> 434 -0.0832116148 1.1683055303 1.748909023 0.4106167657 #> 435 0.4639267373 0.0176269782 -0.323739283 -0.0249793922 #> 436 0.3153427643 0.1968412538 0.297764793 -0.3039828524 #> 437 1.3323659610 1.1833202295 0.117299682 -1.8719901694 #> 438 -0.3193678977 -0.8370973446 -0.377563344 0.9184755016 #> 439 -0.6204523338 -0.0718608214 -0.049270240 -0.2355571408 #> 440 1.7140663681 -0.1027218539 -0.364470275 -0.6822101098 #> 441 -0.6592895406 -3.8238870922 -3.445265650 1.3482752127 #> 442 1.9091456991 0.1094721277 0.107554720 0.2468237048 #> 443 0.0755031232 1.7269960070 2.030227355 0.0305703663 #> 444 -0.5390916016 0.9069451004 1.392550606 0.3156307953 #> 445 -0.0763500983 0.5008019028 0.740670340 -0.1193795189 #> 446 -0.0947008661 -0.7775112333 -0.378722644 0.6294095672 #> 447 -0.7092386416 2.3810561905 2.610861357 -0.8251043546 #> 448 0.0274547172 -0.6351171188 -0.559090018 0.4447649613 #> 449 -3.4938710319 0.4791489283 0.277657381 -0.7860268320 #> 450 0.8844889488 2.8696593495 2.213195425 -1.6852967546 #> 451 1.2534049120 -1.4836162482 -1.312330705 0.6662054083 #> 452 0.6691205893 -1.5159561341 -1.176182025 0.5750321208 #> 453 1.5184719107 0.1221472327 0.481478338 0.5287590025 #> 454 -1.0373180160 0.6236212271 1.535989664 0.5620585874 #> 455 -0.7714622880 -0.2072375811 0.348001712 0.4293597846 #> 456 -0.1169579173 -0.5251265707 -0.352018275 0.0789271415 #> 457 -0.5614108264 1.3869330708 0.296752396 -1.8669780140 #> 458 -1.4658804728 2.9201744873 2.538855205 -1.6840984555 #> 459 -1.1359181676 -0.7193724932 -0.360821125 0.6975076820 #> 460 -0.4276828785 -0.8993423663 -0.254382448 1.0392666944 #> 461 -0.7735442977 0.2024807164 -0.603161818 -1.4166034403 #> 462 0.4163032319 0.3597513314 0.773153500 0.8779510637 #> 463 1.5326106370 1.8134772650 0.893983360 -2.1019119205 #> 464 -0.7167785697 0.5306633894 1.033484965 0.9572808828 #> 465 -0.8154305649 -0.5904980754 -0.858174446 0.0202221193 #> 466 -1.0144247681 0.9329584132 0.404129107 -0.5607129809 #> 467 -0.7318181844 -1.1962825605 -0.100223826 2.5070383757 #> 468 -1.8453004250 -0.9009120323 -0.403344746 1.2176879731 #> 469 1.3870885854 -0.7012518980 -0.960796822 -0.1518644253 #> 470 -1.7467760961 -0.2343899389 -0.021119812 0.2596499921 #> 471 -1.2970987911 -1.2152250863 -1.589095516 -0.8417944691 #> 472 -0.6623913430 -1.2052222597 -0.767787239 0.9356664583 #> 473 1.0996540984 -0.5710242949 -0.367498394 0.3670024870 #> 474 -1.0948033239 -0.7663466524 -0.865196910 0.6727049909 #> 475 0.3566826989 -1.7709173698 -2.113397069 0.1006528806 #> 476 0.6492101058 0.8592629997 0.720058663 0.1889318799 #> 477 0.9689532264 0.7495438495 0.635040039 -0.8923396041 #> 478 1.7007627843 0.3314883613 0.655509542 0.9933737873 #> 479 1.1993005619 1.3029769033 0.693490794 -1.3237515340 #> 480 -0.1500743042 0.6328349666 0.499738198 -0.6900237450 #> 481 0.5979976053 -1.0153624730 -1.061801848 0.0168207668 #> 482 -0.9928389088 1.4986734233 2.222078795 0.2732860747 #> 483 0.6354194133 0.0449804106 0.450477444 0.4548369987 #> 484 0.5975407876 -0.3824700559 0.312395743 1.3283006949 #> 485 0.2404603292 -0.6435384591 0.010159232 1.3547963990 #> 486 -0.9290082873 0.6115281446 0.979411524 0.8089188105 #> 487 -0.5105971387 -0.0727964314 -0.997812258 -1.2806916032 #> 488 -0.2311911458 -0.4545535930 -0.019298367 0.9492647282 #> 489 -0.7237933526 -0.3616559314 -1.059738831 -0.9812868488 #> 490 -2.4105663041 1.4126962127 1.973259654 0.0035498854 #> 491 -1.1583324765 -0.7107568467 -0.406755691 0.8037657044 #> 492 1.9852747606 0.0828622798 0.609941850 0.5856199895 #> 493 -0.8408902318 -0.1243277115 -0.328863381 -0.2184652939 #> 494 0.1579296250 -0.2329566633 -0.014279813 0.6690834390 #> 495 -0.0613177540 0.9456869080 0.987397606 -0.3159404009 #> 496 -0.7841736993 0.5377045450 0.433629649 -0.3861994868 #> 497 -0.9096832833 -0.3474134275 0.106037915 0.7553339890 #> 498 0.2468158566 -0.9061444538 -1.168358492 -0.3849403759 #> 499 0.1750499771 0.7238555700 0.009476823 -1.5959764452 #> 500 -0.2841790773 -1.8918450577 -1.740480605 1.0731143517 #> 501 1.3044256709 1.4741584346 1.887350836 -0.3694761355 #> 502 0.7948228638 -1.0285468613 -1.017446315 0.4212327561 #> 503 1.1886186729 -2.0256381001 -1.787939649 0.6246191072 #> 504 -0.4912705952 -1.0827762837 0.180941733 1.3508941412 #> 505 -0.8124736469 -0.8530918356 -0.178065382 0.5778358567 #> 506 0.3693651313 0.0476089453 0.132283708 0.0872234498 #> 507 -0.9215013884 -1.8451862908 -0.514346598 2.1009528733 #> 508 0.5096286003 -0.0975276551 0.316117671 0.6955295280 #> 509 1.2594178723 1.9401694143 1.722740945 -1.4014851259 #> 510 -2.1010216479 -0.3432872937 -0.760484485 -0.1893832663 #> 511 1.5395901955 -0.4750771419 -1.085109748 -0.3909975626 #> 512 0.2183582808 0.9583743730 0.041738039 -2.3306842516 #> 513 1.7552000237 -0.1925857383 -0.506577415 -0.4068469877 #> 514 -0.7753387302 0.0596751475 -1.083517682 -1.5427498280 #> 515 -1.6245955956 1.4151686262 1.937086769 0.2015833558 #> 516 -1.0025285823 -2.0293041900 -2.945392134 -1.1527377591 #> 517 -2.1453336875 0.6574007512 0.434307088 0.1266418181 #> 518 -0.0922502622 -1.1643416151 -1.145531760 0.2277435434 #> 519 0.0025403809 1.5096024166 2.083169312 0.6040794429 #> 520 1.6110085847 0.4540982199 1.474742208 1.6646537598 #> 521 0.9968168118 0.3893105520 0.567220345 0.0981453711 #> 522 0.2627224346 -0.6511599231 0.233953444 1.3436786042 #> 523 -0.6995011987 0.3678536370 0.358466596 0.0807081165 #> 524 1.8441719823 -1.4410218386 -0.531263727 1.5183015015 #> 525 1.3297458484 -1.0979522025 -0.469124381 1.2610213259 #> 526 0.6789518102 0.7481040128 0.886099625 0.4325220184 #> 527 -1.2274096192 0.4772752028 1.310976368 0.9034869503 #> 528 -0.3734748332 1.9396931125 2.360986577 0.0495707540 #> 529 -0.5911746899 0.6832511272 1.196287406 0.9863760671 #> 530 0.6107274366 -0.2444192174 -0.377007071 0.1561519039 #> 531 -0.3724498642 0.4459706057 1.049556137 1.0911240751 #> 532 0.1566416633 -1.6502878054 -1.536898828 0.5441093646 #> 533 -0.3364111759 -1.4237459239 -1.021588234 1.2255158216 #> 534 0.3260204547 1.0178046475 1.502469702 1.0754718171 #> 535 1.1598297661 0.4756513689 0.920276708 0.8247597402 #> 536 0.6554945729 -0.3729172644 -0.463539207 -0.1547347582 #> 537 1.0429722566 0.5797611106 1.137885637 0.7676123393 #> 538 -0.3007149967 -0.8743458812 -0.769593369 0.4327040299 #> 539 -0.6470825287 -1.8160434383 -1.594156463 0.4766732382 #> 540 -1.3042977710 -0.4696564375 -0.053524779 0.9636035711 #> 541 -0.5040035511 1.3504208065 2.009321096 0.9079172148 #> 542 -0.6854980593 -1.9831829626 -1.372403740 1.3913191676 #> 543 0.6400849904 -0.1105197075 0.146678756 0.3654752357 #> 544 1.1545063746 1.9001920065 1.088932545 -1.4844124026 #> 545 -0.5536701674 -0.0451630009 -0.388878423 -0.1491018689 #> 546 0.2448451932 -1.0648436827 -1.195746084 0.0153615060 #> 547 -0.4913264427 -1.0420877053 -0.227543846 1.5903748172 #> 548 0.9089644049 -0.1770320379 -0.710944597 -0.8591128912 #> 549 -1.4480886049 0.4730045494 1.718534884 1.2888682151 #> 550 1.1290782321 0.1367610712 0.464622596 0.2568278032 #> 551 1.1592133181 0.2630603038 0.743203022 0.8852096757 #> 552 -0.4417653023 0.2765783374 -0.598622595 -1.5280363965 #> 553 0.8421254078 -0.6173575282 -0.240395892 0.9044368833 #> 554 0.2193056555 1.5048842364 1.253244569 -0.9775184146 #> 555 0.2230189795 -1.0376108478 -0.805230549 0.7326335803 #> 556 0.2218885393 1.1586871948 0.207964974 -1.4397610958 #> 557 0.4118867959 1.6318253055 0.980655721 -1.2914290881 #> 558 -1.4991407534 -0.7200037362 0.381143042 1.4909826860 #> 559 0.3373152269 -0.9130749806 -1.369939542 0.0496168734 #> 560 1.6264893201 -0.2471882375 0.118611293 0.6202511068 #> 561 -0.7127661082 0.5691360371 0.208407489 -1.2477273909 #> 562 -0.7147363088 -1.3377610171 -0.771861723 1.0100582351 #> 563 -0.1864531023 0.3745124236 0.081363392 -0.5113616930 #> 564 1.2703105629 0.6329692899 0.937485544 0.2066546330 #> 565 0.6715115868 -1.4026014371 -0.530886203 1.8757254748 #> 566 -0.2935353680 1.4030908311 0.827795196 -1.1142051299 #> 567 0.7157704192 -0.1667592920 0.202055324 0.6641135071 #> 568 -0.0777541743 -0.5284464406 -0.667341663 -0.2572392735 #> 569 -0.9287632275 0.8260561942 0.070102632 -1.2430548917 #> 570 -0.3854772316 -0.0754859197 -0.324002801 -0.3335127044 #> 571 1.1006506323 0.6405915387 0.999909004 0.2944852796 #> 572 0.2680982866 -1.8411781186 -2.013433327 -0.1432620442 #> 573 -0.6783623211 -0.5343196411 -0.332660869 -0.0985449689 #> 574 1.2509244301 -2.1688132978 -1.510598843 1.8536389799 #> 575 -1.2131812235 1.2807124620 0.725632733 -0.8930670837 #> 576 1.0160959012 0.4281976364 0.339545456 -0.6305178197 #> 577 -0.5054480299 -1.4065961300 -1.369956588 0.0643886395 #> 578 -0.6950899022 0.5589463602 0.687815848 -0.0813452606 #> 579 -0.8740647739 -0.6874977354 -0.722830460 -0.1358448282 #> 580 0.5647821482 0.1284506568 -0.779091747 -0.8775250073 #> 581 -0.5599329697 1.1509443637 0.865275448 -0.3248129126 #> 582 1.0685176674 0.8042340665 0.772010795 -0.7248837972 #> 583 1.0578666390 0.9839777411 0.797985013 -0.5382368418 #> 584 -1.3180109144 0.6000965847 0.932220883 0.1983195302 #> 585 -2.0589108575 -0.3678046329 -1.074369988 -0.7923278064 #> 586 0.4032351738 0.1527296615 -0.312658916 -0.6284244236 #> 587 -2.1233863948 -0.1244076262 0.002743405 -0.1860135623 #> 588 0.5578353624 -0.9737917906 -0.347662099 0.7259950026 #> 589 0.4961458538 -0.2345845940 0.606748859 1.3908139548 #> 590 0.3164937916 0.1095979736 0.874649305 1.3544397157 #> 591 0.3380497008 -0.8024833497 0.261773133 1.9617006561 #> 592 1.0665751542 -0.0775564886 0.049889589 0.8019540157 #> 593 -0.5374398433 -0.7934627379 0.664968629 2.1177225395 #> 594 -0.6314629514 -0.0756081177 -0.082993659 0.7454231006 #> 595 -1.0560442250 0.5405547198 1.068572641 1.0120329497 #> 596 -0.5326743862 -0.2900671751 -0.811500487 -0.5712436457 #> 597 1.2863228674 0.5145789536 0.670557638 -0.2672647506 #> 598 0.0451731991 0.5359798380 -0.913268041 -2.0143420187 #> 599 -0.6952671335 0.9167701569 1.296589519 0.0969991237 #> 600 -2.7363006285 0.8750903881 1.341544384 -0.0889496301 #> 601 -0.8758952247 0.7624869355 1.868896989 1.4319349426 #> 602 0.5856062243 -0.7874949460 0.341749096 1.9132939272 #> 603 -0.9627503743 0.5122745815 0.267183382 -0.1321589636 #> 604 -0.4004473246 -0.5201327942 -0.569102764 -0.1176197285 #> 605 -0.9512244044 -0.7586787053 0.498967067 2.3052886341 #> 606 -0.4967838039 0.6304347512 -0.151588104 -1.1962975988 #> 607 -0.1693381631 -1.1538830384 -1.046646379 0.0999659471 #> 608 1.0366938162 2.0621762793 2.303472620 -0.5211401944 #> 609 0.7002720048 -1.2636568923 -1.632009761 0.4224003105 #> 610 -0.1266833485 0.4818373479 0.996164369 0.7321988059 #> 611 -2.0142889482 -0.5995118269 0.908322890 2.0221320575 #> 612 -1.5493455344 -0.5780376546 -0.530444702 -0.1611701799 #> 613 0.3923936965 0.5347873253 0.404909135 -0.4883024285 #> 614 0.8386451785 1.7248576839 1.713506421 -0.3519541475 #> 615 -0.6352838502 -1.5343419429 -1.271865187 0.5115024002 #> 616 0.6385819103 0.0941732392 0.282074129 0.1818724395 #> 617 0.2390371330 -0.4993828955 -1.178027552 -0.8780417978 #> 618 -0.6906692610 0.2680308484 1.174965724 1.2094065570 #> 619 -0.0420338677 1.9270788558 1.636393812 -1.1422986270 #> 620 0.2438808618 -1.7167411081 -1.684191471 1.2505777297 #> 621 1.3403313458 -0.6375432763 -1.495083628 -1.2495368455 #> 622 0.5933776897 -0.6417354929 -1.383198124 -0.5424497287 #> 623 -0.9054844650 -1.5823517743 -1.916929018 0.6338039583 #> 624 -0.8363539920 0.7292392503 -0.459239036 -2.2470600140 #> 625 1.0435909186 -0.6326780265 -0.986488214 -0.2389315239 #> 626 -1.6888842705 0.0906626829 1.138325376 1.4454500460 #> 627 -1.2103858466 -2.1717818435 -1.656641172 1.2892885841 #> 628 -0.2161294458 -0.4529931514 -1.202511724 -1.4088157346 #> 629 1.5501080042 0.9428268138 0.624794239 -0.6831640499 #> 630 -0.5092518713 -0.9818011534 -0.468948783 1.4566801204 #> 631 -0.2538721942 -1.1363627552 -1.223901623 0.3351313578 #> 632 -0.7363730604 0.6591487022 0.065980147 -1.0978451286 #> 633 0.6196165736 0.0688217354 0.337886694 0.5631528890 #> 634 -1.2576689270 -0.3227084778 0.275945726 0.7394384420 #> 635 1.1438301679 -0.2607950489 -0.489690886 -0.1797010911 #> 636 0.6665801522 0.2752522039 -0.575208101 -0.8628676103 #> 637 -0.7779399411 -0.2957134073 -0.676270888 -0.1543121439 #> 638 -0.3174705628 -0.2876014994 -0.195753221 -0.0147844657 #> 639 0.0864920385 -0.1114925948 0.064005245 0.4322262735 #> 640 0.6248883181 -0.1252694088 -1.130721335 -1.7468014202 #> 641 0.5787570894 -0.3241654155 -1.438406227 -0.9848176167 #> 642 -0.0594945663 0.4681470721 0.254021136 -0.0988252739 #> 643 -0.4978049328 -0.1814643368 0.676679999 1.4778311711 #> 644 1.5987086390 0.9752029619 1.301187825 -0.2199095282 #> 645 0.7507220816 0.9141130936 0.974713443 -0.4262853793 #> 646 -1.9242827544 0.6372644734 1.385255851 0.3576828180 #> 647 -0.7556012249 -0.6707785611 -1.314285794 -1.0033586091 #> 648 0.6574948316 -1.4837559485 -1.198395666 1.4890065415 #> 649 -0.9284846394 -1.1943612132 -0.192686110 2.0598346952 #> 650 0.6881453583 -0.9472139562 -1.164422747 -0.0064190080 #> 651 -0.0737933303 1.3200024913 0.492163370 -1.4247363837 #> 652 -0.6375413001 -0.0117008853 0.705823144 1.2069505603 #> 653 -0.0072406190 -1.1453500912 -0.899155278 0.1120799746 #> 654 0.3579303035 -0.5713767399 -0.765019674 0.2104561359 #> 655 0.9975946589 0.0635987036 -0.741743976 -1.6419483739 #> 656 -1.4545079032 1.7352662912 1.927148459 0.2858529641 #> 657 0.4672351818 -0.5172862222 -0.536265374 -0.1625385985 #> 658 -1.2059303701 0.7636857751 0.841590751 0.4475778366 #> 659 -2.2392674216 0.1426994592 0.109498801 0.1611965206 #> 660 0.0761683182 -2.1800811254 -1.605700160 2.6222409273 #> 661 -0.7320082355 1.1865119188 1.803049669 0.3801084141 #> 662 -1.2613655111 1.5137301076 1.879506082 -0.0961759653 #> 663 0.4737278786 -0.1713876468 -0.471088131 -0.3741098931 #> 664 -0.4055584280 0.1204510495 1.102389391 1.0057796996 #> 665 -0.1096022712 1.8190858470 2.078385193 0.0490648166 #> 666 0.5912071964 0.1397610528 -0.088196608 -0.2876619846 #> 667 1.1641364546 -0.0757056626 0.159043797 0.2794683046 #> 668 -1.0857452902 0.7766007912 0.932163585 -0.1217333823 #> 669 1.9171136144 0.3087094123 -0.745446199 -1.6397749473 #> 670 0.0170002664 -1.6255035948 -1.085908303 1.6770147049 #> 671 -0.1679750862 0.2188835215 -0.725215446 -1.8860166588 #> 672 -0.0004898524 -0.9507636818 0.030599124 1.2803563216 #> 673 0.8560495538 -0.5626965983 -0.531928418 0.1590499599 #> 674 0.5039646392 -1.2135577566 -1.188876360 0.1476039136 #> 675 -2.2131237848 -0.3804082015 -0.494096219 0.0157931646 #> 676 0.0908022944 0.3255100808 1.088526216 0.8613148382 #> 677 -0.0901713832 -1.4184309801 -1.349146691 0.7339305209 #> 678 0.2559094371 -0.6603128583 -0.688647120 0.6402630094 #> 679 -0.1540698886 -1.4214172334 -1.050288731 0.9220425830 #> 680 1.5290031321 -0.1950978921 -0.206804667 0.0366715348 #> 681 -0.4022421474 -0.0050045210 0.639909589 0.6613532981 #> 682 -0.5542071076 0.9275224175 0.005314208 -1.5498301457 #> 683 0.5748780270 -0.5386533707 -0.452243249 0.1848732614 #> 684 -1.2624707495 -2.8106352440 -2.658669605 1.3234982252 #> 685 0.3633090782 0.6153150646 -0.086036227 -1.7243890623 #> 686 -0.4040273435 -0.4371513441 -0.673780076 -0.3948567500 #> 687 1.9498044688 -1.9841562894 -1.777805084 1.1746266622 #> 688 -0.6932043386 1.4678731670 1.348024719 -0.0625178832 #> 689 -0.7801379943 0.1922283623 0.010336005 -0.4056508624 #> 690 1.3452618806 -0.0408907299 0.378443518 0.2249384303 #> 691 -0.2943885488 -0.2383488361 0.188819961 0.8542047196 #> 692 -1.5717567614 0.9402004493 1.332366830 0.0305555848 #> 693 -2.1004821015 -1.2843292428 -1.472686158 -0.1106693781 #> 694 -0.8784274832 -0.7233297661 -0.434060198 0.8479727118 #> 695 -1.5129183778 -0.0405561668 -0.148710075 0.2020458706 #> 696 -1.1778306588 1.1600092724 0.853135724 -0.2167217853 #> 697 -0.8587384852 -1.9091341931 -1.566507222 0.9264831784 #> 698 -0.6340944306 0.7051015394 0.536705108 -0.3628398703 #> 699 -0.5538425324 0.6090848315 -0.143648880 -1.9533402716 #> 700 -0.3652173107 1.7243308216 1.399882950 -0.8847730330 #> 701 0.9380645706 -0.6681644882 -0.944637839 -0.4521769889 #> 702 -0.3886544525 -1.2359756154 -0.701395919 0.6828929636 #> 703 0.3122813603 0.6117717749 0.387678688 -0.5416466344 #> 704 -2.8786319712 1.5605366986 1.281749304 -0.8939656552 #> 705 -0.2115104500 -0.4446517237 0.194670403 1.5176599013 #> 706 -0.2715415174 -2.1365342190 -1.474615634 2.0679749134 #> 707 1.8209351661 -0.3440435296 -1.244210938 -0.7863259752 #> 708 -1.2155194238 -0.6786410592 -1.299201207 -0.8524052448 #> 709 1.0158802244 0.9132581515 0.774353026 -1.0777305053 #> 710 1.2420296448 0.0940418482 -0.183688685 -0.7128086666 #> 711 -1.4237357212 0.5896739735 -0.174977586 -1.1038959233 #> 712 1.6838773758 -0.6097834941 0.412420023 1.7219590383 #> 713 -0.6150120746 0.6032192875 0.551636787 -0.2861403953 #> 714 0.0345827061 -1.0395276531 -1.098235680 0.4486927611 #> 715 -0.2832630001 1.4049611140 1.268026140 -0.3914370025 #> 716 0.1545002475 0.2002355762 0.211822034 0.3048403977 #> 717 -1.0708844925 1.7471167559 1.010392562 -1.5973520043 #> 718 -0.6694954477 0.9363904231 1.221383798 0.0432159008 #> 719 -0.7457406705 1.6353906920 1.512354224 -0.8705524509 #> 720 -0.3943234952 1.1926616578 1.723850645 0.1862201853 #> 721 -1.0836001293 0.2551578604 1.731725264 1.9840885024 #> 722 1.5303626234 0.1650436006 1.094437083 1.1185693914 #> 723 -1.4271946678 -0.6275573210 -0.084302121 0.9110334145 #> 724 -0.9348800870 -0.4075511101 -0.238537516 0.2632035337 #> 725 -1.4210540159 -0.5525190422 -0.368755730 0.2104393511 #> 726 -1.4750597194 0.0740258369 0.377555626 -0.0518100604 #> 727 0.6015020275 -0.6952396229 0.205403034 1.4191190092 #> 728 0.3452907530 -0.1729181277 0.473108676 1.3076180945 #> 729 -0.3147620932 -0.2438576067 0.207279201 0.7527979105 #> 730 -0.6559068994 -1.3696012365 -0.436324210 1.4805027033 #> 731 0.1574756847 0.1305336600 -0.402340136 -1.1620121401 #> 732 0.4732917401 1.1876962205 1.388473689 0.0444150971 #> 733 0.7245961516 -1.5605547019 -1.447815104 0.4266102052 #> 734 0.7327227408 -1.3441438774 -1.135295476 0.5245190050 #> 735 1.1736736647 0.3374315004 0.047029596 -0.4261295135 #> 736 0.1405083868 0.4968418840 1.209248567 1.1283526792 #> 737 -2.5383954227 0.0614587021 0.072391778 0.4571298713 #> 738 -1.1738044838 -0.5128260873 -0.212904404 0.4893807088 #> 739 -1.7466658946 1.2176639125 1.101415169 -0.1077513421 #> 740 0.0983341298 -0.6159515894 -1.290189525 -0.6592716178 #> 741 -1.3684820029 -0.3180995386 0.175467609 0.7607304204 #> 742 1.6643770424 0.6733818362 0.294005626 -1.1656835664 #> 743 1.7382941087 0.3557662018 0.528786009 0.3937590286 #> 744 0.6128476306 -0.1121162100 -0.911360169 -1.3476938996 #> 745 -1.3703182602 -0.3019742783 -1.382649766 -1.1815596867 #> 746 -0.4590141175 0.2979306539 0.269864002 -0.4919737107 #> 747 -0.9368951523 -1.6650059076 -0.596317753 1.6599359653 #> 748 -0.5618429801 -0.9541205404 -0.795756235 0.5547828745 #> 749 -1.3186748349 1.4930732510 1.535849730 -0.5782595810 #> 750 -0.5910729029 -1.0607126586 -0.349196081 1.4349338392 #> 751 0.8772163560 0.6422399667 0.028027431 -1.4548577113 #> 752 1.9414024711 2.0489002445 1.800250402 -1.1808979936 #> 753 -0.2252302817 0.0702681641 0.645210798 1.2543749153 #> 754 -0.1988164646 0.5486887607 0.444806045 -0.0908102542 #> 755 -2.2347950031 0.3385869469 -0.211156719 -1.0388754295 #> 756 -0.4419874235 1.1413519376 0.762348427 -0.7869199465 #> 757 -0.9300438139 0.0799854609 -0.230864934 -0.4518375999 #> 758 1.5427556172 1.0619417078 0.715928407 -1.1323897776 #> 759 0.8141802414 0.3885047425 1.246753501 1.3405466314 #> 760 -0.4850482031 -2.5092919869 -2.613137098 0.4286810221 #> 761 -1.7911321464 -1.3757647841 -1.403690704 -0.1158081964 #> 762 0.2552024791 -0.6170574435 -0.940809538 -0.3130405144 #> 763 -2.0301790965 0.0723832204 0.267741855 -0.3048146667 #> 764 1.7792536970 -0.7763482923 -0.044160373 1.2583076860 #> 765 1.7536000605 -1.0131957490 -0.357635770 1.5622384207 #> 766 0.5932820209 -0.8846594588 -0.559528638 0.9299626396 #> 767 1.0750088042 -0.0795076836 -0.591101412 -0.3845543714 #> 768 1.0266259614 -0.1665758940 -0.237733589 0.3359773395 #> 769 1.8838743666 0.5969922946 0.711060822 0.3523694155 #> 770 -1.1929078766 0.6742725587 -0.587503819 -2.4587582359 #> 771 0.1732576741 -0.5168705078 -0.753063904 -0.5101097308 #> 772 -1.9013221756 -0.1755818258 -0.407408877 -0.5927905910 #> 773 -0.4490646136 -0.4058911142 0.126903798 1.2596924689 #> 774 0.0449848225 0.4470448722 -0.071841376 -1.3047358838 #> 775 1.5480093198 0.1648328848 0.229152426 -0.1178934138 #> 776 0.4533917260 -0.3311256703 -0.566735447 -0.6227409419 #> 777 -0.7664692649 -0.5767755906 -0.562657412 0.0623121260 #> 778 -0.0097612207 0.1411269815 0.483088120 0.6899912062 #> 779 -0.4556498995 -0.8367992472 -0.668926255 0.3989580327 #> 780 -0.7176960976 -0.1567775988 -0.325636629 -0.4023945104 #> 781 -0.9082690526 -0.1501156508 0.283500046 0.6950326997 #> 782 1.3621695818 2.4616947658 2.044579163 -0.7207314114 #> 783 0.6613768337 0.5510749737 1.234486041 0.9465005888 #> 784 -0.5189683829 -0.6123261485 -0.275076919 0.6932369584 #> 785 -1.4259202467 -1.6630024274 -1.738985134 -0.0938968856 #> 786 -1.1513141263 -0.7170150432 -0.912500204 -0.2061299796 #> 787 2.1121673244 -0.9219781412 -0.790497068 0.7961278523 #> 788 -0.6389747397 -0.6677667513 -1.587622046 -1.4320268444 #> 789 -0.6917576180 -1.3078628192 -1.755805533 -0.5159557415 #> 790 0.7055479276 -1.2679117775 -0.648310275 1.1583485023 #> 791 0.7296099146 1.0009617957 0.697399678 -0.8626039748 #> 792 -0.2807591606 -0.1715994390 -0.474466607 -1.0114773826 #> 793 0.5208296262 -0.2012131572 -0.230770942 0.2899214498 #> 794 0.2984108629 -0.6484486229 -0.688540956 0.6120314610 #> 795 1.0419865121 -1.8474004685 -1.675974156 0.5564894681 #> 796 -1.3908030830 0.8035005041 0.286180252 -1.7340490575 #> 797 0.4195653155 -1.5458376590 -1.531649386 0.8146693973 #> 798 1.4284384294 0.7394024048 -0.263897236 -1.5664717965 #> 799 0.6243304040 -0.2305038316 0.554058330 1.6699897267 #> 800 0.4946065489 2.3445544521 1.870087946 -1.1967339897 #> 801 -0.2824653374 -0.7366760203 -0.318097300 0.7652781478 #> 802 0.1313703439 -0.8621065364 -0.519424016 0.4721759154 #> 803 -0.3273622739 -2.3174256700 -1.957967721 1.2337201257 #> 804 -0.7480279864 -1.2412483335 -0.279471901 1.5963747945 #> 805 -0.9272512850 1.0786583086 0.761496444 -0.7252813910 #> 806 0.7084775046 -2.4548442508 -3.628313100 -0.7454158655 #> 807 0.4439906679 0.9909901115 1.012125448 -0.2695933269 #> 808 0.3972109201 -0.0417435169 -0.325020715 -0.6278572727 #> 809 1.1010864990 1.6755809265 1.372531635 -1.1357838675 #> 810 1.5495821209 -1.1358316257 -0.810165079 0.4424896119 #> 811 -1.0185613030 -1.0144391009 -0.759725728 0.7043249735 #> 812 0.4416434813 1.7736246291 0.749401592 -2.4286608179 #> 813 0.5038520562 0.0788402858 0.600572433 0.5154254609 #> 814 -0.8825714892 0.8589226302 0.325846790 -1.4037060630 #> 815 -0.3179779669 0.5531742042 0.704613210 0.2092886342 #> 816 -1.4997620707 1.3205270707 0.671195636 -1.2144674113 #> 817 0.7964842432 0.8354260126 0.182167192 -1.1059150703 #> 818 -1.3291084191 2.7570257861 2.702697678 -0.3506612548 #> 819 1.4866019618 -0.7563113191 -0.859906243 0.1927489685 #> 820 0.5820567072 -1.0499269653 -0.239170025 1.4993575153 #> 821 -0.7061669535 0.0865956414 0.200690590 0.3420561735 #> 822 1.5735533431 1.2606061690 0.972263674 -0.5290902000 #> 823 -1.6038536585 0.9062336500 0.448471640 -0.5063419930 #> 824 0.8494854303 -1.0558514524 -1.348198749 -0.1856978959 #> 825 1.2463696637 -0.0996032204 -0.836291364 -1.6193047126 #> 826 0.3244265031 -2.1858317298 -1.229468726 2.2056510444 #> 827 1.3728210268 0.7152008431 0.009959350 -1.0752309665 #> 828 -1.5916222753 1.0469488287 1.391879926 -0.1932908952 #> 829 1.7360131061 0.2448835577 -0.649019210 -1.7408968212 #> 830 -0.0060375295 -0.1848613300 0.183672525 0.4172360029 #> 831 -1.4563188435 -1.5108957550 -2.194099855 -1.1740927542 #> 832 -0.0420660207 0.2537061766 -0.407922534 -0.9505314871 #> 833 0.9628787738 -0.8937053937 -0.484679152 0.8282125233 #> 834 -0.6548214043 1.8206252470 1.515583414 -0.6111362964 #> 835 -0.2300650596 -1.0441826251 -0.911280789 0.2881066394 #> 836 -0.5137799601 -0.9779747797 -0.726227764 0.2750370703 #> 837 0.5302170356 0.6320324782 0.031084272 -0.9335748166 #> 838 2.2130514427 1.7424035860 1.309458225 -1.3979211388 #> 839 -0.7015524598 -1.4507402517 -1.520473564 0.4244247419 #> 840 1.2693293416 0.6392141181 1.336446257 0.7898404988 #> 841 -0.4831102690 0.7446660430 1.288373819 0.1796532118 #> 842 0.2887953308 -0.7710383350 -0.924091244 0.4007222659 #> 843 -1.6835705400 0.7919072952 1.047587066 -0.2503956257 #> 844 -1.1936059622 -1.0358134455 -1.006124067 0.2227655879 #> 845 1.0394144247 -0.9762313292 -0.858704669 0.6983826136 #> 846 0.2884344093 -0.6252740325 0.346877263 1.8229745025 #> 847 0.4072897555 -0.1171133850 0.451473714 0.6034868006 #> 848 0.4286372186 -0.8283062627 -0.887288241 0.0108428832 #> 849 -2.2061380527 0.0313344034 0.423583538 0.9884434226 #> 850 -0.7926383090 -1.2841961954 -0.615988436 0.8239554969 #> 851 0.8507887064 -0.5460128730 -1.476084147 -1.6387697879 #> 852 0.8808798516 0.9630566397 1.092435173 0.1218794873 #> 853 -0.3128484540 0.7785429286 0.612289072 -0.4735490723 #> 854 0.2686795324 0.3146427576 0.468891299 0.3298819834 #> 855 -0.6163263202 -0.7551812721 -0.167303398 1.5104659520 #> 856 2.1797557099 -1.1282012324 -0.863036222 0.7244408110 #> 857 -0.2730971962 2.1030337915 1.600279653 -1.7222169503 #> 858 -0.2263926228 -0.3693750422 0.679805577 1.5903476996 #> 859 -0.7633529632 -1.4666474093 -1.346359966 0.3483337597 #> 860 -1.3688573668 -0.7161702306 -0.395538306 0.8160923023 #> 861 -0.1288171773 0.1996713073 0.767091896 0.6646674252 #> 862 -0.3083049975 -1.0616047886 -1.772572135 -0.0443515046 #> 863 -0.1016505588 -1.5265829061 -1.324467466 1.0585615601 #> 864 -0.1620229996 -1.1548553672 -1.243338312 0.5463070649 #> 865 -0.4707424886 -2.5690630034 -2.469974880 0.7838326916 #> 866 0.8661921528 0.3961274681 0.679270493 0.3029985638 #> 867 0.3028708896 -0.0094293454 0.118892568 0.1746146559 #> 868 -0.8111268971 0.9684982891 0.111346330 -1.0379809937 #> 869 0.2851594402 1.4498009044 0.867231043 -1.4297744700 #> 870 -0.2251104022 2.7578751077 2.181331359 -1.8913893379 #> 871 0.1368509725 3.0693999644 1.936068677 -2.2138115625 #> 872 0.4985284119 -0.0719259483 0.609127607 1.4429568117 #> 873 -2.0106137323 1.1712080092 1.643143296 1.0533661269 #> 874 -0.1829939793 -2.0836991515 -2.062435291 0.3976284881 #> 875 0.4124240196 -0.0935285905 -0.010163707 0.1029853685 #> 876 -0.9335673396 0.2874848806 -0.721668978 -1.9596475755 #> 877 -0.2092973704 -0.7775340297 -0.622405469 0.5827398665 #> 878 -0.0267124000 0.5805054737 0.597145121 0.3269908977 #> 879 -1.8352046585 1.1539049328 1.760503855 1.1623150623 #> 880 -0.0456786470 -0.8569683150 -1.048150555 -0.2295626443 #> 881 -1.2060442957 -0.6979701377 -1.070950053 -0.6153376535 #> 882 0.9789629182 1.5620044030 1.049713701 -1.1520624067 #> 883 0.1010927119 0.7296496270 1.236504864 0.4627969810 #> 884 -0.1793136345 0.8798665483 0.876778375 0.0761608563 #> 885 0.3112573440 -0.5946462142 -1.051424527 -0.4873484607 #> 886 0.5437623223 0.2983138616 0.377306008 0.5938705772 #> 887 -2.0398248523 -0.1690534778 -1.253473923 -1.2669718763 #> 888 0.1631469085 1.0393516766 2.099425192 1.2687901252 #> 889 0.5996762529 0.4564583027 -0.966419850 -2.5151274499 #> 890 0.4407786448 -0.3083160055 0.134900472 0.3608898227 #> 891 0.1837076561 -1.0553439477 -0.585816897 1.3945187859 #> 892 -2.1923669386 0.5159484295 0.403855111 -0.3979677923 #> 893 0.6073626851 -1.5219919644 -1.664031058 0.6972003463 #> 894 -1.3194838376 -1.5963982717 -1.056968012 1.4982818932 #> 895 -0.6656003989 0.9811794915 -0.327045817 -2.4489032260 #> 896 1.2892205466 -0.0270730880 0.283903898 0.1872387106 #> 897 0.4389043238 -1.2490850331 -0.354510036 2.0011448732 #> 898 1.8108836996 0.4963849558 0.166791512 -1.1747283936 #> 899 -0.4311966657 0.1019209507 -0.071849951 -0.4337880369 #> 900 0.4708149510 -0.1385399887 -0.304215015 -0.3230860979 #> 901 0.5573213793 -1.0489214811 -0.734034926 0.2004882163 #> 902 0.1855787789 0.4909039493 2.002255975 2.7573648256 #> 903 0.9018878242 -0.4215955600 -0.764166292 -0.2712003376 #> 904 -0.2130973883 -0.9185198717 -0.737543323 0.5473558157 #> 905 -0.0474423429 -0.1434392857 -0.104374862 -0.3134263383 #> 906 -1.2946449292 0.5140625698 0.919450811 0.9352097011 #> 907 -1.9924701889 0.1005192336 0.212437284 0.4029697848 #> 908 -0.8873021182 0.4736888266 -0.067848813 -1.4028619661 #> 909 -1.2745557355 1.4149802806 1.487309021 -0.0774356636 #> 910 -1.3683707437 1.9522230980 1.350987610 -1.7320542997 #> 911 -0.1967469250 -0.0056673718 -0.111794048 -0.0841171955 #> 912 0.5517750618 0.9022387504 0.723668436 -0.5354535754 #> 913 0.0782811899 0.6606829694 0.195393643 -0.2530835851 #> 914 -0.7508076748 0.5284569297 0.772694665 -0.4506761376 #> 915 -0.3396081941 -0.0981753841 -0.198752924 0.3614229608 #> 916 0.2609764110 1.2849329712 1.142301189 -0.6414186638 #> 917 0.2524393073 -0.9671571750 -0.704052197 0.6298719151 #> 918 0.0630523056 0.0042374056 0.100350166 0.0640418343 #> 919 0.6678230148 -1.0407229306 -0.683698164 0.6521823892 #> 920 0.2433243279 0.4493970172 0.483234134 -0.1217209646 #> 921 -1.6970964549 -2.5575885885 -2.713300120 0.5700121170 #> 922 0.3441936961 1.2156798605 0.701546511 -1.1476598902 #> 923 0.6455136434 0.6300481445 0.115451338 -0.2524077901 #> 924 -0.7089728864 -0.3736422768 -0.012939102 0.2212622616 #> 925 -0.4841715206 -1.6788117326 -1.035312799 1.7081416116 #> 926 -1.2635203716 0.9556309214 1.213375825 0.1190602660 #> 927 -0.7213123189 -0.0527329796 0.413122635 0.5015636087 #> 928 -1.4294247702 0.4427589803 0.323869075 -0.2619797587 #> 929 0.9822535394 -0.6434358983 -1.115309830 -1.1209537650 #> 930 -1.6579670226 1.3713889518 1.400622005 -0.2174936721 #> 931 -1.4205875904 -0.3087522107 0.102247784 0.4273958770 #> 932 0.4310844605 -0.7607476069 -0.789561338 0.6681964789 #> 933 0.7762627740 -0.0486183912 -0.590502739 -1.0326233077 #> 934 0.9666903035 0.2790414969 0.171449744 -0.1562848425 #> 935 -1.2634211792 -1.0517956415 -0.551255788 1.0536575436 #> 936 0.0973634539 -1.1582368045 -0.956025035 0.8588950576 #> 937 -1.0858533575 -0.6844814443 -0.007352250 1.2403270766 #> 938 1.0227450297 0.7303251580 0.701861787 -0.4109947417 #> 939 -0.2352751770 -0.3585984685 0.034527568 1.0306089414 #> 940 0.4258553370 1.4705095719 1.644539372 -0.2400348882 #> 941 -0.0557223973 0.0333765437 0.346276446 0.8844330078 #> 942 0.1401301413 -0.4758220528 -0.302618019 0.2300095821 #> 943 0.0465151576 -2.0098283303 -1.535085260 1.1564979653 #> 944 -0.1891369786 0.0040558621 -0.957295024 -1.8457170669 #> 945 1.4929759134 1.0331332306 0.213463458 -1.3803621307 #> 946 -0.6304366043 0.7287388844 0.473908404 -0.2323081500 #> 947 0.8768511704 -0.7559963745 0.586279159 2.2840924999 #> 948 -1.3085993802 0.7004715444 0.564710074 -0.1349677926 #> 949 -0.1109679775 0.5358508097 1.087650284 1.3880163565 #> 950 0.6204873652 1.5928530207 1.896645677 -0.2941840516 #> 951 -0.4601902603 -0.4330332317 0.128301454 0.4213490900 #> 952 -0.3915820370 0.0338751543 -0.728645801 -0.8319587388 #> 953 -1.0408651683 0.1707294872 -0.213702489 -0.9693475273 #> 954 -0.0367321540 -1.0286598793 -1.889471299 -0.9507735576 #> 955 0.4346744743 2.4295851919 2.790949126 0.2900830442 #> 956 -0.7876451846 0.5218989013 0.618789753 0.0206063413 #> 957 0.3384012758 1.3221983709 1.069794857 -0.6626404882 #> 958 -1.3831608621 -0.5426091838 -1.045239345 -0.7939578030 #> 959 -0.2749337495 -2.7681164127 -2.513734334 1.5236256459 #> 960 0.6579269250 0.2974833000 0.101918446 -0.4342141292 #> 961 -0.6771577902 0.2543842901 0.493471278 0.1401768508 #> 962 0.9007992072 1.3917072717 0.342982123 -1.9895021932 #> 963 0.4458374094 0.1018223777 0.017999544 -0.2570169573 #> 964 -0.1693277702 1.4433207655 2.081982558 0.4934128210 #> 965 -0.7352733505 -0.2625306165 -0.138356828 0.7671370027 #> 966 0.9351357739 1.8758047902 1.208696263 -1.7178642320 #> 967 -1.0515867373 0.8191829437 1.175206265 0.1421878663 #> 968 -0.9541786695 0.0290683759 0.460697730 0.6895890374 #> 969 1.2742100302 0.7234406338 -0.163889281 -1.7197893491 #> 970 -1.1532097322 0.4497584586 0.435971450 -0.2672792190 #> 971 1.2856473038 0.0842571579 -0.248254038 -0.9375633407 #> 972 0.1367775527 1.5500171163 0.756371746 -1.6851122123 #> 973 -1.4722725972 -1.1749441859 -1.765852547 -0.4805081685 #> 974 0.8613533736 -0.3570904526 -1.252350408 -0.9302867854 #> 975 -0.0899002219 -0.3006434466 -0.119820214 0.4990886155 #> 976 0.1296033005 0.7132651022 0.593727036 -0.3523063514 #> 977 -0.3851816270 -0.2791414087 0.261212304 0.9912653998 #> 978 -1.5026312506 1.0374722481 0.629147414 -0.4988912716 #> 979 -0.5336793021 -0.1596112805 -0.222750332 0.3315287052 #> 980 2.0509146991 0.3799065213 0.519045354 0.5244240378 #> 981 0.9393312575 -1.8652101656 -1.743940546 0.7258215156 #> 982 1.0424927447 -1.6018147985 -1.630369745 0.3861080698 #> 983 -0.2937500560 -1.5110521403 -1.619767144 -0.2090171634 #> 984 -0.4271312040 0.7398844848 0.135389180 -1.4893692036 #> 985 -0.9575948113 1.7025196105 0.967290422 -2.1670858811 #> 986 1.0613645759 0.7478613689 1.206752528 0.4456106606 #> 987 -0.8173118759 -0.1079795094 0.197474110 0.6242578430 #> 988 -0.3990432618 -0.1733378125 0.193949862 0.7589317976 #> 989 -0.7261482843 -0.1671368174 -0.017705953 0.3927015008 #> 990 -0.7101145456 0.3014745648 -0.230666864 -0.7806266277 #> 991 0.7997868118 1.1995363904 1.507301299 -0.0789141760 #> 992 0.3212550843 -0.0636215169 0.063647318 0.9408785566 #> 993 -0.5356825003 0.3206007781 1.166512790 1.7034893053 #> 994 0.6859275918 0.2471914430 0.123476848 -0.5304519446 #> 995 0.1962488287 -0.1216908057 -0.198403881 0.0002219803 #> 996 2.4831838168 -0.0782187395 1.473723808 1.8571316049 #> 997 0.5747718967 -2.8189659519 -2.905952605 0.4046344439 #> 998 -1.0648583323 0.1998847209 -0.517541832 -0.9332636902 #> 999 -0.2312225216 -0.2999658454 -0.204561509 -0.1774115591 #> 1000 0.5015486388 0.7851524817 0.724885412 -0.2499866497"},{"path":"http://svmiller.com/reference/corvectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Create multivariate data by permutation — corvectors","title":"Create multivariate data by permutation — corvectors","text":"corvectors() function obtain multivariate dataset specifying relation specified variables.","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create multivariate data by permutation — corvectors","text":"","code":"corvectors( data, corm, tol = 0.005, conv = 10000, cores = 2, splitsize = 1000, verbose = FALSE, seed )"},{"path":"http://svmiller.com/reference/corvectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create multivariate data by permutation — corvectors","text":"data data matrix containing data corm value containing desired correlation vector data matrix containing desired correlations tol single value vector tolerances length ncol(data) - 1. default 0.005 conv maximum iterations allowed. Defaults 1000. cores number cores used parallel computing splitsize size use splitting data verbose Logical statement. Default FALSE seed optional seed set","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create multivariate data by permutation — corvectors","text":"corvectors() returns matrix given specified multivariate relation.","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create multivariate data by permutation — corvectors","text":"liberally copy-pasted van Kooten Vink's wonderful---longer-supported correlate package. call correlate() package, opt corvectors() .","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create multivariate data by permutation — corvectors","text":"Pascal van Kooten Gerko Vink","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create multivariate data by permutation — corvectors","text":"","code":"if (FALSE) { # \\dontrun{ set.seed(8675309) library(tibble) # bivariate example, start with zero correlation as_tibble(data.frame(corvectors(replicate(2, rnorm(100)), .5))) # multivariate example as_tibble(data.frame(corvectors(replicate(4, rnorm(100)), c(.5, .6, .7)))) } # }"},{"path":"http://svmiller.com/reference/db_lselect.html","id":null,"dir":"Reference","previous_headings":"","what":"Lazily select variables from multiple tables in a relational database — db_lselect","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"db_lselect() allows select variables multiple tables SQL database. returns lazy query combines variables together one data frame (tibble). user can choose run collect() query see fit.","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"","code":"db_lselect(.data, connection, vars)"},{"path":"http://svmiller.com/reference/db_lselect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":".data character vector tables relational database connection name connection object vars variables (entered class \"character\") select tables database","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"Assuming particular structure database, function returns combined table including requested variables tables listed data character vector. returned table attributes inherited dplyr interfaces SQL, allowing user extract information query (e.g. show_query()).","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"wrapper function purrr dplyr heavy lifting. tables database declared character (character vector). variables select also declared character (character vector), wrapped one_of() function within select() dplyr.","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"Miller, Steven V. 2020. \"Clever Uses Relational (SQL) Databases Store Wider Data (Assistance dplyr purrr)\" http://svmiller.com/blog/2020/11/smarter-ways--store--wide-data--sql-magic-purrr/","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"","code":"# \\donttest{ library(DBI) library(RSQLite) library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:stevemisc’: #> #> tbl_df #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union library(dbplyr) #> #> Attaching package: ‘dbplyr’ #> The following objects are masked from ‘package:dplyr’: #> #> ident, sql set.seed(8675309) A <- data.frame(uid = c(1:10), a = rnorm(10), b = sample(letters, 10), c = rbinom(10, 1, .5)) B <- data.frame(uid = c(11:20), a = rnorm(10), b = sample(letters, 10), c = rbinom(10, 1, .5)) C <- data.frame(uid = c(21:30), a = rnorm(10), b = sample(letters, 10), c = rbinom(10, 1, .5), d = rnorm(10)) con <- dbConnect(SQLite(), \":memory:\") copy_to(con, A, \"A\", temporary=FALSE) copy_to(con, B, \"B\", temporary=FALSE) copy_to(con, C, \"C\", temporary=FALSE) # This returns no warning because columns \"a\" and \"b\" are in all tables c(\"A\", \"B\", \"C\") %>% db_lselect(con, c(\"uid\", \"a\", \"b\")) #> # Source: SQL [?? x 3] #> # Database: sqlite 3.41.2 [:memory:] #> uid a b #> #> 1 1 -0.997 f #> 2 2 0.722 z #> 3 3 -0.617 y #> 4 4 2.03 x #> 5 5 1.07 c #> 6 6 0.987 p #> 7 7 0.0275 e #> 8 8 0.673 i #> 9 9 0.572 o #> 10 10 0.904 n #> # ℹ more rows # This returns two warnings because column \"d\" is not in 2 of 3 tables. # ^ this is by design. It'll inform the user about data availability. c(\"A\", \"B\", \"C\") %>% db_lselect(con, c(\"uid\", \"a\", \"b\", \"d\")) #> Warning: Unknown columns: `d` #> Warning: Unknown columns: `d` #> # Source: SQL [?? x 4] #> # Database: sqlite 3.41.2 [:memory:] #> uid a b d #> #> 1 1 -0.997 f NA #> 2 2 0.722 z NA #> 3 3 -0.617 y NA #> 4 4 2.03 x NA #> 5 5 1.07 c NA #> 6 6 0.987 p NA #> 7 7 0.0275 e NA #> 8 8 0.673 i NA #> 9 9 0.572 o NA #> 10 10 0.904 n NA #> # ℹ more rows dbDisconnect(con) # }"},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":null,"dir":"Reference","previous_headings":"","what":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"data illustrate labeled data process get_var_info() package.","code":""},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"","code":"ess9_labelled"},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"data frame 109 observations following 4 variables. essround numeric constant edition another numeric constant cntry character vector (label) country data netusoft numeric vector (label) self-reported internet consumption respondent","code":""},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"Data condensed summaries raw data. amount every unique combination country self-reported internet consumption. data illustrate get_var_info() function package.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":null,"dir":"Reference","previous_headings":"","what":"Reorganize a factor after ","title":"Reorganize a factor after ","text":"fct_reorg() forcats hack reorganizes factor re-leveling . situationally useful coefficient plots years.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reorganize a factor after ","text":"","code":"fct_reorg(fac, ...)"},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reorganize a factor after ","text":"fac character factor vector ... optional parameters supplied forcats functions.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reorganize a factor after ","text":"function takes character factor vector first re-levels re-coding certain values. end result factor.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reorganize a factor after ","text":"Solution comes way issue Github: https://github.com/tidyverse/forcats/issues/45","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reorganize a factor after ","text":"","code":"x<-factor(c(\"a\",\"b\",\"c\")) fct_reorg(x, B=\"b\", C=\"c\") #> [1] a B C #> Levels: B C a"},{"path":"http://svmiller.com/reference/filter_refs.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"filter_refs() convenience function wrote filtering data frame citations returning entries valid .bib entry (character vector). wrote easily passing citations print_refs() function also included package.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"","code":"filter_refs(bibdat, criteria, type = \"bibtexkey\")"},{"path":"http://svmiller.com/reference/filter_refs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"bibdat data frame citations, like one created bib2df package criteria criteria, specified character vector, filter data frame citations type particular type citation entry filter. Defaults \"bibtexkey\" (filters based column unique citation keys). type == \"year\", function filters character vector years.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"filter_refs() takes data frame citations, like one created bib2df package, returns character vector (amounting valid .bib entry) citations user wants. can easily passed print_refs() function also included package.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"filter_refs() assumes familiarity BibTeX, .bib entries, depends bib2df package.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"","code":"# Based on `stevepubs` configuration, filter on `BIBTEXKEY` where # the citation key matches one of these. filter_refs(stevepubs, c(\"miller2017etst\", \"miller2017etjc\", \"miller2013tdpi\")) #> @ARTICLE{miller2013tdpi, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Journal of Peace Research}, #> NUMBER = {6}, #> PAGES = {677--690}, #> TITLE = {Territorial Disputes and the Politics of Individual Well-Being}, #> VOLUME = {50}, #> YEAR = {2013}} #> #> @ARTICLE{miller2017etjc, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Research Quarterly}, #> NUMBER = {4}, #> PAGES = {790--802}, #> TITLE = {The Effect of Terrorism on Judicial Confidence}, #> VOLUME = {70}, #> YEAR = {2017}} #> #> @ARTICLE{miller2017etst, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Behavior}, #> NUMBER = {2}, #> PAGES = {457--478}, #> TITLE = {Economic Threats or Societal Turmoil? Understanding Preferences for Authoritarian Political Systems}, #> VOLUME = {39}, #> YEAR = {2017}} #> # Based on `stevepubs` configuration, filter on `YEAR` where # the publication year is 2017, 2018, 2019, 2020, or 2021. filter_refs(stevepubs, c(2017:2021), type = \"year\") #> @ARTICLE{curtismiller2021snp, #> AUTHOR = {K. Amber Curtis and Steven V. Miller}, #> JOURNAL = {European Union Politics}, #> NUMBER = {2}, #> PAGES = {202--26}, #> TITLE = {A (Supra)Nationalist Personality? The Big Five's Effects on Political-Territorial Identification}, #> VOLUME = {22}, #> YEAR = {2021}} #> #> @ARTICLE{gibleretal2020icm, #> AUTHOR = {Douglas M. Gibler and Steven V. Miller and Erin K. Little}, #> JOURNAL = {International Studies Quarterly}, #> NUMBER = {2}, #> PAGES = {476--479}, #> TITLE = {The Importance of Correct Measurement}, #> VOLUME = {64}, #> YEAR = {2020}} #> #> @ARTICLE{miller2017etjc, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Research Quarterly}, #> NUMBER = {4}, #> PAGES = {790--802}, #> TITLE = {The Effect of Terrorism on Judicial Confidence}, #> VOLUME = {70}, #> YEAR = {2017}} #> #> @ARTICLE{miller2017etst, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Behavior}, #> NUMBER = {2}, #> PAGES = {457--478}, #> TITLE = {Economic Threats or Societal Turmoil? Understanding Preferences for Authoritarian Political Systems}, #> VOLUME = {39}, #> YEAR = {2017}} #> #> @ARTICLE{miller2017ieea, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Conflict Management and Peace Science}, #> NUMBER = {5}, #> PAGES = {526--545}, #> TITLE = {Individual-Level Expectations of Executive Authority under Territorial Threat}, #> VOLUME = {34}, #> YEAR = {2017}} #> #> @ARTICLE{miller2018etttc, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Peace Economics, Peace Science and Public Policy}, #> NUMBER = {1}, #> TITLE = {External Territorial Threats and Tolerance of Corruption: A Private/Government Distinction}, #> VOLUME = {24}, #> YEAR = {2018}, #> DOI = {10.1515/peps-2017-0043}} #> #> @ARTICLE{miller2019wata, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Social Science Quarterly}, #> NUMBER = {1}, #> PAGES = {272--288}, #> TITLE = {What Americans Think About Gun Control: Evidence from the General Social Survey, 1972-2016}, #> VOLUME = {100}, #> YEAR = {2019}} #> #> @ARTICLE{millerdavis2020ewsp, #> AUTHOR = {Steven V. Miller and Nicholas T. Davis}, #> JOURNAL = {Journal of Race, Ethnicity, and Politics}, #> NUMBER = {2}, #> PAGES = {334--351}, #> TITLE = {The Effect of White Social Prejudice on Support for American Democracy}, #> VOLUME = {6}, #> YEAR = {2021}} #> #> @INCOLLECTION{milleretal2020gtc, #> AUTHOR = {Steven V. Miller and Jaroslav Tir and John A. Vasquez}, #> BOOKTITLE = {Oxford Research Encyclopedia of International Studies}, #> PUBLISHER = {Oxford University Press}, #> TITLE = {Geography, Territory, and Conflict}, #> YEAR = {2020}, #> DOI = {10.1093/acrefore/9780190846626.013.320}} #>"},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":null,"dir":"Reference","previous_headings":"","what":"French Leader-Years, 1874-2015 — fra_leaderyears","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"data generated peacesciencer French leader-years 1874 2015. going use data stress-testing calculation -called \"peace spells\" data decidedly imbalanced, .","code":""},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"","code":"fra_leaderyears"},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"data frame 255 observations following 10 variables. obsid unique observation ID Archigos data ccode Correlates War state code France (220) leader name—typically last name—leader year observation year leader startdate start date leader's period office enddate end date leader's period office gmlmidongoing ongoing inter-state dispute leader? gmlmidonset new inter-state dispute onset leader? gmlmidongoing_init ongoing inter-state dispute leader leader initiated? gmlmidonset_init new inter-state dispute onset leader leader initiated?","code":""},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"Data generated development version (scheduled release v. 0.7) peacesciencer. Conflict data come GML MID data (v. 2.2.1). Leader data come Archigos (v. 4.1).","code":""},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"Goemans, Henk E., Kristian Skrede Gleditsch, Giacomo Chiozza. 2009. \"Introducing Archigos: Dataset Political Leaders\" Journal Peace Research 46(2): 269–83. Gibler, Douglas M., Steven V. Miller, Erin K. Little. 2016. “Analysis Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Simulations from a Model Object (with New Data) — get_sims","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"get_sims() function simulate quantities interest multivariate normal distribution \"new data\" regression model.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"","code":"get_sims(model, newdata, nsim, seed)"},{"path":"http://svmiller.com/reference/get_sims.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"model model object newdata data frame quantities interest simulated nsim Number simulations run seed optional seed set","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"get_sims() returns data frame (tibble) quantities interest identifying information particular simulation number.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"() flexible function takes merMod object (estimated lme4, blme, etc.) lm glm object generates quantities interest paired new data observations interest. note: really tested function linear models, generalized linear models, mixed model equivalents. mixed models, approach offer support incorporation random effects random slopes. just fixed effects, typically people want anyway. Users want better incorporate random intercepts slope find support merTools package.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"","code":"if (FALSE) { # \\dontrun{ # Note: these models are dumb, but they illustrate how it works. M1 <- lm(mpg ~ hp, mtcars) # Note: this function requires the DV to appear somewhere, anywhere in the \"new data\" newdat <- data.frame(mpg = 0, hp = c(mean(mtcars$hp) - sd(mtcars$hp), mean(mtcars$hp), mean(mtcars$hp) + sd(mtcars$hp))) get_sims(M1, newdat, 100, 8675309) # Note: this is likely a dumb model, but illustrates how it works. mtcars$mpgd <- ifelse(mtcars$mpg > 25, 1, 0) M2 <- glm(mpgd ~ hp, mtcars, family=binomial(link=\"logit\")) # Again: this function requires the DV to be somewhere, anywhere in the \"new data\" newdat$mpgd <- 0 # Note: the simulations are returned on their original \"link\". Here, that's a \"logit\" # You can adjust that accordingly. `plogis(y)` will convert those to probabilities. get_sims(M2, newdat, 100, 8675309) library(lme4) M3 <- lmer(mpg ~ hp + (1 | cyl), mtcars) # Random effects are not required here since we're passing over them. get_sims(M3, newdat, 100, 8675309) } # }"},{"path":"http://svmiller.com/reference/get_var_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a small data frame of the variable label and values. — get_var_info","title":"Get a small data frame of the variable label and values. — get_var_info","text":"get_var_info() allows peek labelled data, extracting given column's variable labels. intended use mostly \"peeking\" purpose recoding column's absence codebook form documentation. gvi() shortcut function.","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a small data frame of the variable label and values. — get_var_info","text":"","code":"get_var_info(.data, x) gvi(...)"},{"path":"http://svmiller.com/reference/get_var_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a small data frame of the variable label and values. — get_var_info","text":".data data frame x column within data frame ... optional, make shortcut (gvi) work","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a small data frame of the variable label and values. — get_var_info","text":"column data frame labelled, function returns message communicating absence labels. column data frame labelled, function returns small data frame communicating var_label() output (var), (often always) numeric \"code\" coinciding label (code), \"label\" attached (label).","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get a small data frame of the variable label and values. — get_var_info","text":"function leans var_label() val_label() labelled package, dependency package. function designed used \"pipe.\"","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a small data frame of the variable label and values. — get_var_info","text":"","code":"library(tibble) library(dplyr) library(magrittr) ess9_labelled %>% get_var_info(netusoft) # works, as intended #> var code label #> 1 Internet use, how often 1 Never #> 2 Internet use, how often 2 Only occasionally #> 3 Internet use, how often 3 A few times a week #> 4 Internet use, how often 4 Most days #> 5 Internet use, how often 5 Every day #> 6 Internet use, how often 7 Refusal #> 7 Internet use, how often 8 Don't know #> 8 Internet use, how often 9 No answer ess9_labelled %>% get_var_info(cntry) # works, as intended #> var code label #> 1 Country GB United Kingdom #> 2 Country BE Belgium #> 3 Country DE Germany #> 4 Country EE Estonia #> 5 Country IE Ireland #> 6 Country BG Bulgaria #> 7 Country CH Switzerland #> 8 Country FI Finland #> 9 Country SI Slovenia #> 10 Country NL Netherlands #> 11 Country PL Poland #> 12 Country NO Norway #> 13 Country FR France #> 14 Country RS Serbia #> 15 Country AT Austria #> 16 Country IT Italy #> 17 Country HU Hungary #> 18 Country CY Cyprus #> 19 Country CZ Czechia ess9_labelled %>% get_var_info(ess9round) # barks at you; data are not labelled #> get_var_info() requires a labelled column. Otherwise, you get this message."},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"theme_steve(), now stevethemes, preferred theme mine years ago. basically theme_bw() ggplot2 theme, tweaking things. moved theme_steve_web() things now, prominently website. theme incorporates \"Open Sans\" \"Titillium Web\" fonts like much. post_bg() legacy function changing backgrounds plots better match background color website. theme_steve_ms() LaTeX manuscripts use cochineal font package. theme_steve_font() purpose, allowing supply font.","code":""},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"","code":"theme_steve_web(...) post_bg(...) theme_steve_ms(axis_face = \"italic\", caption_face = \"italic\", ...) theme_steve_font(axis_face = \"italic\", caption_face = \"italic\", font, ...)"},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"... optional stuff, put anything . need . axis_face font face (\"plain\", \"italic\", \"bold\", \"bold.italic\"). Optional, defaults \"italic\". Applicable theme_steve_ms(). caption_face font face (\"plain\", \"italic\", \"bold\", \"bold.italic\"). Optional, defaults \"italic\". Applicable theme_steve_ms(). font font family plot. Applicable theme_steve_font().","code":""},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"post_bg() takes ggplot2 plot changes background color \"#fdfdfd\". theme_steve_web() extends theme_steve() add custom fonts, notably \"Open Sans\" \"Titillium Web\". cases, functions take ggplot2 plot return another ggplot2 plot, cosmetic changes. theme_steve_ms() takes ggplot2 plot overlays \"Crimson Pro\" fonts, basis cochineal font package LaTeX. theme_steve_font() takes ggplot2 plot overlays font choosing.","code":""},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"theme_steve_web() theme_steve_ms() explicitly depend fonts installed end. ultimately optional use functions imply . functions remain understood \"legacy\" functions longer maintained updated. stevethemes package ggplot2 elements going forward.","code":""},{"path":[]},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"","code":"if (FALSE) { # \\dontrun{ library(ggplot2) ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_web() + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice the prettier fonts, if you have them.\", caption = \"Data: ?mtcars in {datasets} in base R.\") ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_web() + post_bg() + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice the slight change in background color\", caption = \"Data: ?mtcars in {datasets} in base R.\") ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_ms() + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice the fonts will match the 'cochineal' font package in LaTeX.\", caption = \"Data: ?mtcars in {datasets} in base R.\") ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_font(font = \"Comic Sans MS\") + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice that this will look ridiculous\", caption = \"Data: ?mtcars in {datasets} in base R.\") } # }"},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":null,"dir":"Reference","previous_headings":"","what":"German Dyad-Years, 1816-2020 — gmy_dyadyears","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"data generated peacesciencer German (Prussian) dyad-years 1816 2020. going useful stress-testing \"peace spell\" calculations may look like huge gap years. Correlates War context, Germany disappears international system 1945 1990. 'll also serve nice test making sure spell calculations misbehave context missing data. application, data disputes 2011 2020, dyad-years include 2011 2020.","code":""},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"","code":"gmy_dyadyears"},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"data frame 11174 observations following 6 variables. dyad unique identifier dyad ccode1 Correlates War state code Germany (255) ccode2 Correlates War state code state dyad year observation year dyad gmlmidongoing ongoing inter-state dispute dyad-year? gmlmidonset new inter-state dispute onset dyad-year","code":""},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"Data generated development version (scheduled release v. 0.7) peacesciencer. Conflict data come GML MID data (v. 2.2.1).","code":""},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"Gibler, Douglas M., Steven V. Miller, Erin K. Little. 2016. “Analysis Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the Only Reproducible Seed That Matters — jenny","title":"Set the Only Reproducible Seed That Matters — jenny","text":"jenny() sets reproducible seed 8675309. reproducible seed use.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the Only Reproducible Seed That Matters — jenny","text":"","code":"jenny(x = 8675309)"},{"path":"http://svmiller.com/reference/jenny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the Only Reproducible Seed That Matters — jenny","text":"x vector","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the Only Reproducible Seed That Matters — jenny","text":"x specified 8675309, function sets reproducible seed 8675309 returns nice message congratulating . x 8675309, function sets reproducible seed gently admonishes wasting time.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the Only Reproducible Seed That Matters — jenny","text":"jenny() comes additional perks emo package installed. package optional.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the Only Reproducible Seed That Matters — jenny","text":"","code":"jenny() # will work and reward you for it #> Jenny, I got your number... jenny(12345) # will not work and will result in a stern message #> Why are you using this function with some other reproducible seed..."},{"path":"http://svmiller.com/reference/linloess_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"linloess_plot() provides visual diagnostic linearity assumption OLS model. Provided OLS model fit lm() base R, function extracts model frame creates faceted scatterplot. facet, linear smoother LOESS smoother estimated points. Users run function can assess just much linear smoother LOESS smoother diverge. diverge, user can determine much OLS model good fit specified. plot also point potential outliers may need consideration.","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"","code":"linloess_plot( mod, resid = TRUE, smoother = \"loess\", se = TRUE, span = 0.75, suppress_warning = TRUE, ... ) # S3 method for class 'linloess' print(x, ...)"},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"mod fitted OLS model resid logical, defaults TRUE. FALSE, y-axis plots raw values dependent variable. TRUE, y-axis model's residuals. Either work well matter hand, provided treat output illustrative suggestive. smoother defaults \"loess\", passed 'method' argument non-linear smoother. se logical, defaults TRUE. TRUE, gives standard error estimates assorted smoothers. resid TRUE, standard error flat line 0. span numeric, defaults .75. adjustment smoother. Higher values permit smoother lines might warranted presence sparse pockets data. suppress_warning logical, defaults TRUE. TRUE, plot suppresses assorted warnings LOESS smoother otherwise cautioning things eyes otherwise see. ... additional arguments (ignored) x ggplot object special 'linloess' class","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"linloess_plot() returns faceted scatterplot ggplot2 object. linear smoother solid blue (blue standard error bands) LOESS smoother dashed black line (gray/default standard error bands). can add cosmetic features fact. function may spit warnings related LOESS smoother, depending data. think fine extent really just visual aid informal diagnostic linearity assumption.","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"function makes implicit assumption variable regression formula name \".y\" \".resid\". may interest (sake rudimentary diagnostic checks) disable standard error bands particularly ill-fitting linear models.","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Linear Smoother to LOESS Smoother for Your OLS Model — linloess_plot","text":"","code":"M1 <- lm(mpg ~ am + carb + disp, data=mtcars) linloess_plot(M1) #> `geom_smooth()` using formula = 'y ~ x' #> `geom_smooth()` using formula = 'y ~ x' linloess_plot(M1, color=\"black\", pch=21) #> `geom_smooth()` using formula = 'y ~ x' #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"http://svmiller.com/reference/make_perclab.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"make_perclab() takes proportion, multiplies 100, optionally rounds , pastes percentage sign next .","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"","code":"make_perclab(x, d = 2)"},{"path":"http://svmiller.com/reference/make_perclab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"x numeric vector d digits round. Defaults 2.","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"function takes proportion, multiplies 100, (optionally) rounds set decimal point, pastes percentage sign next .","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"function useful modeling proportions something like bar chart (proportions flexible) want label bar percentage. function mostly cosmetic.","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"","code":"x <- runif(100) make_perclab(x) #> [1] \"15.95%\" \"47.82%\" \"76.48%\" \"76.97%\" \"26.85%\" \"67.3%\" \"97.88%\" \"84.63%\" #> [9] \"85.67%\" \"44.52%\" \"83.82%\" \"58.33%\" \"51.1%\" \"26.02%\" \"74.95%\" \"91.82%\" #> [17] \"71.64%\" \"20.62%\" \"81.69%\" \"71.59%\" \"6.06%\" \"84.71%\" \"84.68%\" \"33.26%\" #> [25] \"55.97%\" \"66.95%\" \"25.46%\" \"7.92%\" \"16%\" \"81.64%\" \"97.57%\" \"84.21%\" #> [33] \"32.93%\" \"59.26%\" \"18.39%\" \"45.15%\" \"44.02%\" \"35.81%\" \"20.39%\" \"72.15%\" #> [41] \"97.65%\" \"94.42%\" \"51.75%\" \"84.29%\" \"34.3%\" \"3.89%\" \"31.81%\" \"13.59%\" #> [49] \"33.91%\" \"76.23%\" \"75.28%\" \"43.36%\" \"75.5%\" \"40.68%\" \"70.32%\" \"31.16%\" #> [57] \"42.62%\" \"22.76%\" \"64.91%\" \"14.96%\" \"64.66%\" \"27.46%\" \"87.57%\" \"79.56%\" #> [65] \"94.24%\" \"78.67%\" \"72.19%\" \"86.53%\" \"26.93%\" \"0.19%\" \"40.95%\" \"74.39%\" #> [73] \"52.55%\" \"86.38%\" \"37.7%\" \"82.4%\" \"40.19%\" \"37.85%\" \"43.89%\" \"43.71%\" #> [81] \"26.26%\" \"44.05%\" \"49.85%\" \"85.97%\" \"51.82%\" \"11.51%\" \"75.3%\" \"16.19%\" #> [89] \"40.05%\" \"18.23%\" \"44%\" \"74.73%\" \"19.08%\" \"95.5%\" \"2.4%\" \"23.09%\" #> [97] \"59.39%\" \"23.55%\" \"51.44%\" \"64.3%\""},{"path":"http://svmiller.com/reference/make_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"make_scale() rescale vector user-defined minimum maximum.","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"","code":"make_scale(x, minim, maxim)"},{"path":"http://svmiller.com/reference/make_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"x numeric vector minim desired numeric minimum maxim desired numeric maximum","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"function takes numeric vector returns rescaled version observed (desired) minimum, observed (desired) maximum, rescaled values extremes.","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"function useful wanted kind minimum-maximum rescaling variable given scale, prominently rescaling minimum 0 maximum 1 (thinking ahead regression). function flexible enough minimum maximum.","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"","code":"x <- runif(100, 1, 100) make_scale(x, 2, 5) # works #> [1] 3.691435 2.107502 3.670319 2.808087 2.615673 2.354313 2.035948 3.128916 #> [9] 3.787428 2.048508 2.514866 3.244003 2.778355 4.685685 4.348520 3.125769 #> [17] 4.614064 3.065012 3.651240 4.612916 4.896847 2.237435 4.184424 3.979520 #> [25] 3.335907 2.936188 2.354956 2.975470 3.351972 4.891430 4.500238 3.191101 #> [33] 3.957162 4.667745 2.537577 2.117305 2.272307 4.773745 3.803732 4.907440 #> [41] 3.106993 2.655200 4.768845 3.331101 3.252278 4.399584 2.000000 2.862249 #> [49] 2.534993 4.341187 4.848127 2.970642 2.520859 2.757290 2.593809 3.852103 #> [57] 4.917708 2.434919 2.254518 2.785304 3.631706 2.729471 4.742084 4.935707 #> [65] 4.232052 3.276069 3.551173 3.827683 4.342697 4.850768 2.397893 2.485624 #> [73] 4.386657 4.911031 4.597821 2.292243 3.615681 4.947668 2.276199 2.769858 #> [81] 3.763996 4.694063 4.213002 4.505863 2.531546 4.016657 2.649579 3.645823 #> [89] 4.208062 2.575016 4.158535 4.681841 4.639911 3.903428 3.686708 2.344965 #> [97] 3.054974 3.248985 5.000000 4.889466 make_scale(x, 5, 2) # results in message #> The desired minimum should not be greater than or equal to the desired maximum. Try again. make_scale(x, 0, 1) # probably why you're using this. #> [1] 0.56381179 0.03583408 0.55677310 0.26936218 0.20522434 0.11810428 #> [7] 0.01198278 0.37630528 0.59580925 0.01616923 0.17162203 0.41466759 #> [13] 0.25945173 0.89522844 0.78283997 0.37525631 0.87135473 0.35500402 #> [19] 0.55041347 0.87097216 0.96561569 0.07914503 0.72814125 0.65983986 #> [25] 0.44530222 0.31206271 0.11831883 0.32515659 0.45065717 0.96380997 #> [31] 0.83341280 0.39703383 0.65238741 0.88924841 0.17919234 0.03910175 #> [37] 0.09076887 0.92458181 0.60124387 0.96914665 0.36899770 0.21839989 #> [43] 0.92294842 0.44370037 0.41742612 0.79986144 0.00000000 0.28741634 #> [49] 0.17833088 0.78039566 0.94937583 0.32354748 0.17361974 0.25243013 #> [55] 0.19793622 0.61736765 0.97256942 0.14497303 0.08483947 0.26176803 #> [61] 0.54390194 0.24315704 0.91402786 0.97856894 0.74401739 0.42535642 #> [67] 0.51705782 0.60922758 0.78089907 0.95025615 0.13263085 0.16187452 #> [73] 0.79555246 0.97034368 0.86594029 0.09741447 0.53856031 0.98255591 #> [79] 0.09206622 0.25661945 0.58799873 0.89802115 0.73766743 0.83528771 #> [85] 0.17718193 0.67221891 0.21652650 0.54860782 0.73602058 0.19167198 #> [91] 0.71951174 0.89394686 0.87997035 0.63447593 0.56223588 0.11498833 #> [97] 0.35165797 0.41632849 1.00000000 0.96315523"},{"path":"http://svmiller.com/reference/map_quiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"simple data set records every wrong guess map quiz assignments gave intro IR class Clemson University across five semesters.","code":""},{"path":"http://svmiller.com/reference/map_quiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"","code":"map_quiz"},{"path":"http://svmiller.com/reference/map_quiz.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"data frame 1772 observations following 8 variables. class ordered factor semester wrong guess recorded student. Levels include \"Spring 2018\", \"Fall 2018\", \"Spring 2019\", \"Fall 2019\", \"Spring 2020.\" students number students class taking map quiz. region region map country located. Values include \"Europe\", \"Africa\", \"Asia\", \"Latin America\", \"MENA.\" \"MENA\" short \"Middle East North Africa.\" country country asked student correctly identify guess country actual state incorrectly guessed student ccode1 Correlates War state code state wanted student identify country. ccode2 Correlates War state code state wrong guess state guess mindist minimum distance (kilometers) country guess","code":""},{"path":"http://svmiller.com/reference/map_quiz.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"Students can always make guess wrong, explains NAs data. Students given five separate numbered maps prompted identify 10 countries . maps never changed across five semesters, prompts. Use data see fit. Obviously, FERPA considerations mean share anything else potential value .","code":""},{"path":"http://svmiller.com/reference/mround.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply a Number by 100 and Round It (By Default: 2) — mround","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"mround() convenience function wrote annotating bar charts make. Assuming proportion variable, mround() multiply value 100 round presentation. default, rounds two. user can adjust .","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"","code":"mround(x, d = 2)"},{"path":"http://svmiller.com/reference/mround.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"x numeric vector d number decimal points user wants round. set, rounds two decimal points.","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"function takes numeric vector, multiplies 100, rounds (two digits default), returns user.","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"sister function make_perclab() package. , however, add percentage sign.","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"","code":"x <- runif(100) mround(x) #> [1] 77.17 86.70 8.91 30.97 62.94 93.54 49.49 18.95 32.14 32.52 58.12 84.82 #> [13] 67.87 52.93 22.46 68.72 14.33 24.87 39.63 61.16 4.70 34.00 60.88 5.18 #> [25] 29.85 34.66 87.61 41.54 76.29 22.50 66.38 40.79 41.30 67.60 95.44 27.72 #> [37] 45.55 16.41 15.38 7.23 93.26 31.99 24.51 53.31 91.62 22.81 12.14 34.06 #> [49] 61.89 85.55 65.54 60.79 49.81 65.56 88.59 26.24 72.44 22.17 31.03 15.95 #> [61] 74.47 83.76 77.94 75.31 23.87 59.91 24.03 64.50 80.73 80.78 31.72 34.27 #> [73] 47.95 3.81 51.50 3.64 47.21 2.44 31.31 72.60 24.25 95.20 4.47 57.24 #> [85] 83.92 96.72 16.90 25.97 30.70 73.46 43.82 85.82 87.88 26.51 70.31 30.54 #> [97] 90.63 6.73 60.41 15.60 mround(x, 2) # same as above #> [1] 77.17 86.70 8.91 30.97 62.94 93.54 49.49 18.95 32.14 32.52 58.12 84.82 #> [13] 67.87 52.93 22.46 68.72 14.33 24.87 39.63 61.16 4.70 34.00 60.88 5.18 #> [25] 29.85 34.66 87.61 41.54 76.29 22.50 66.38 40.79 41.30 67.60 95.44 27.72 #> [37] 45.55 16.41 15.38 7.23 93.26 31.99 24.51 53.31 91.62 22.81 12.14 34.06 #> [49] 61.89 85.55 65.54 60.79 49.81 65.56 88.59 26.24 72.44 22.17 31.03 15.95 #> [61] 74.47 83.76 77.94 75.31 23.87 59.91 24.03 64.50 80.73 80.78 31.72 34.27 #> [73] 47.95 3.81 51.50 3.64 47.21 2.44 31.31 72.60 24.25 95.20 4.47 57.24 #> [85] 83.92 96.72 16.90 25.97 30.70 73.46 43.82 85.82 87.88 26.51 70.31 30.54 #> [97] 90.63 6.73 60.41 15.60 mround(x, 3) #> [1] 77.172 86.698 8.905 30.967 62.935 93.542 49.492 18.947 32.144 32.524 #> [11] 58.119 84.819 67.867 52.925 22.461 68.724 14.332 24.873 39.633 61.155 #> [21] 4.699 33.996 60.877 5.179 29.851 34.662 87.609 41.538 76.293 22.500 #> [31] 66.381 40.790 41.301 67.601 95.436 27.716 45.554 16.414 15.384 7.225 #> [41] 93.260 31.993 24.513 53.315 91.617 22.808 12.136 34.064 61.887 85.549 #> [51] 65.538 60.786 49.807 65.555 88.587 26.241 72.441 22.170 31.025 15.948 #> [61] 74.472 83.756 77.942 75.306 23.872 59.912 24.030 64.500 80.728 80.779 #> [71] 31.717 34.270 47.949 3.805 51.502 3.644 47.212 2.442 31.313 72.599 #> [81] 24.251 95.197 4.468 57.236 83.924 96.723 16.904 25.972 30.699 73.460 #> [91] 43.823 85.825 87.883 26.506 70.314 30.537 90.630 6.727 60.409 15.596"},{"path":"http://svmiller.com/reference/nin.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Non-Matching Elements — %nin%","title":"Find Non-Matching Elements — %nin%","text":"%nin% finds non-matching elements given vector. negation %%.","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Non-Matching Elements — %nin%","text":"","code":"a %nin% b"},{"path":"http://svmiller.com/reference/nin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Non-Matching Elements — %nin%","text":"vector (character, factor, numeric) b vector (character, factor, numeric)","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Non-Matching Elements — %nin%","text":"%nin% finds non-matching elements returns one two things, depending use. two simple vectors, report matches . comparing vector within data frame, effect reporting rows data frame match supplied (second) vector.","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find Non-Matching Elements — %nin%","text":"simple negation %%. use mostly columns data frame.","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find Non-Matching Elements — %nin%","text":"","code":"library(tibble) library(dplyr) # Watch this subset stuff dat <- tibble(x = seq(1:10), d = rnorm(10)) filter(dat, x %nin% c(3, 6, 9)) #> # A tibble: 7 × 2 #> x d #> #> 1 1 -0.319 #> 2 2 0.915 #> 3 4 -1.10 #> 4 5 -0.605 #> 5 7 -2.09 #> 6 8 -0.934 #> 7 10 -0.398"},{"path":"http://svmiller.com/reference/normal_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Make and annotate a normal distribution with ggplot2 — normal_dist","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"normal_dist() convenience function making plot normal distribution annotated areas underneath normal curve.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"","code":"normal_dist(curvecolor, fillcolor, fontfamily)"},{"path":"http://svmiller.com/reference/normal_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"curvecolor color curve . ggplot2-recognized format . fillcolor color area underneath curve . ggplot2-recognized format . fontfamily Font family labeling areas underneath curve. OPTIONAL. can omit like.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"function returns fancy plot normal distribution annotated areas underneath hood. Note whatever color supplied fillcolor automatically lightened areas center curve.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"normal distribution standard normal distribution mean 0 standard deviation 1.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"","code":"library(stevemisc) normal_dist(\"blue\",\"red\") normal_dist(\"purple\",\"orange\")"},{"path":"http://svmiller.com/reference/p_z.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert the p-value you want to the z-value it actually is — p_z","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"loathe statistical instruction privileges obtaining magical p-value reference area underneath standard normal curve, botch actual z-value corresponding magical p-value. simple function converts p-value want (typically .05, thanks R.. Fisher) z-value actually kind claims typically make inferential statistics. going inference wrong way, least get z-value right.","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"","code":"p_z(x, ts = TRUE)"},{"path":"http://svmiller.com/reference/p_z.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"x numeric vector (one multiple) 0 1 ts logical, defaults TRUE. TRUE, returns two-sided critical z-value. FALSE, function returns one-sided critical z-value.","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"function takes numeric vector, corresponding p-value want, returns numeric vector coinciding z-value want standard normal distribution. example, z-value corresponding magic number .05 (conventional cutoff assessing statistical significance) 1.96, something like 1.959964 (rounding default six decimal points).","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"p_z() takes p-value interest converts , precision, z-value actually . function takes vector returns vector. function assumes something akin calculating confidence interval testing regression coefficient null hypothesis zero. means default output two-sided critical z-value. taught use two-sided z-values agnostic direction effect statistic interest, , frank, hilarious given research typically done.","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"","code":"library(stevemisc) p_z(.05) #> [1] 1.959964 p_z(c(.001, .01, .05, .1)) #> [1] 3.290527 2.575829 1.959964 1.644854 p_z(.05, ts=FALSE) #> [1] 1.644854 p_z(c(.001, .01, .05, .1), ts=FALSE) #> [1] 3.090232 2.326348 1.644854 1.281552"},{"path":"http://svmiller.com/reference/prepare_refs.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"prepare_refs last-minute formatting data frame created bib2df can formatted nicely various outputs.","code":""},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"","code":"prepare_refs(bib2df_refs, toformat = \"plain\")"},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"bib2df_refs data frame created bib2df toformat type output ultimately going want print_refs() . Default \"plain\".","code":""},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"print_refs() last-minute formatting data frame created bib2df rendering R Markdown little easier less code-heavy.","code":""},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"function designed work generally absence various fields. Assume, example, data frame BOOK field. function uses one_of() wrapper work around . \"warning\" returned function message. function may expanded think use cases.","code":""},{"path":[]},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"","code":"prepare_refs(stevepubs) #> Warning: Unknown columns: `BOOK` #> Warning: Unknown columns: `MAINTITLE` #> # A tibble: 19 × 12 #> CATEGORY BIBTEXKEY AUTHOR BOOKTITLE JOURNAL NUMBER PAGES PUBLISHER TITLE #> #> 1 ARTICLE millergib… NA *Confl… 3 261-… NA \"Dem… #> 2 ARTICLE giblereta… NA *Compa… 12 1655… NA \"Ind… #> 3 ARTICLE giblermil… NA *Socia… 5 1202… NA \"Com… #> 4 ARTICLE giblermil… NA *Journ… 2 258-… NA \"Qui… #> 5 ARTICLE miller201… NA *Journ… 6 677-… NA \"Ter… #> 6 ARTICLE giblermil… NA *Journ… 5 634-… NA \"Ext… #> 7 ARTICLE giblereta… NA *Inter… 4 719-… NA \"An … #> 8 ARTICLE miller201… NA *Polit… 2 457-… NA \"Eco… #> 9 ARTICLE miller201… NA *Confl… 5 526-… NA \"Ind… #> 10 ARTICLE miller201… NA *Polit… 4 790-… NA \"The… #> 11 ARTICLE miller201… NA *Peace… 1 NA NA \"Ext… #> 12 ARTICLE miller201… NA *Socia… 1 272-… NA \"Wha… #> 13 ARTICLE giblereta… NA *Inter… 2 476-… NA \"The… #> 14 INCOLLECTION millereta… *Oxford … NA NA NA Oxford U… \"Geo… #> 15 ARTICLE millerdav… NA *Journ… 2 334-… NA \"The… #> 16 ARTICLE curtismil… NA *Europ… 2 202-… NA \"A (… #> 17 ARTICLE miller202… NA *The S… NA NA NA \"Eco… #> 18 ARTICLE peacescie… NA *Confl… NA NA NA \"~{ … #> 19 ARTICLE miller202… NA *Journ… NA NA NA \"A R… #> # ℹ 3 more variables: VOLUME , YEAR , DOI "},{"path":"http://svmiller.com/reference/print_refs.html","id":null,"dir":"Reference","previous_headings":"","what":"Print and Format .bib Entries as References — print_refs","title":"Print and Format .bib Entries as References — print_refs","text":"print_refs() convenience function found edited allow user print format .bib entries references. function useful want load .bib entry set entries print middle document R Markdown.","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print and Format .bib Entries as References — print_refs","text":"","code":"print_refs( bib, csl = \"american-political-science-association.csl\", toformat = \"markdown_strict\", cslrepo = \"https://raw.githubusercontent.com/citation-style-language/styles/master\", spit_out = TRUE, delete_after = TRUE )"},{"path":"http://svmiller.com/reference/print_refs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print and Format .bib Entries as References — print_refs","text":"bib valid .bib entry csl CSL file, matching one available Github repository, user wants format references. Default \"american-political-science-association.csl\". toformat output wanted user. Default \"markdown_strict\". cslrepo directory CSL files. Defaults one Github. spit_out logical, defaults TRUE. TRUE, wraps (\"spits \") formatted citations writeLines() output console. FALSE, returns character vector. delete_after logical, defaults TRUE. TRUE, deletes CSL file done. FALSE, retains CSL (potential) future use.","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print and Format .bib Entries as References — print_refs","text":"print_refs() takes .bib entry returns requested formatted reference references .","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print and Format .bib Entries as References — print_refs","text":"print_refs() assumes active internet connection absence appropriate CSL file working directory. citation style language (CSL) file supplied user must match file massive Github repository CSL files. Users interested potential outputs read Pandoc (https://pandoc.org/MANUAL.html). Github repository CSL files available : https://github.com/citation-style-language/styles.","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print and Format .bib Entries as References — print_refs","text":"","code":"# \\donttest{ example <- \"@Book{vasquez2009twp, Title = {The War Puzzle Revisited}, Author = {Vasquez, John A}, Publisher = {New York, NY: Cambridge University Press}, Year = {2009}}\" print_refs(example) #> I'm going to assume this is a .bib entry... #> Downloading CSL from https://raw.githubusercontent.com/citation-style-language/styles/master/american-political-science-association.csl #> Vasquez, John A. 2009. *The War Puzzle Revisited*. New York, NY: #> Cambridge University Press. # }"},{"path":"http://svmiller.com/reference/ps_btscs.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ","title":"Create ","text":"ps_btscs() allows create spells (\"peace years\" international conflict context) observations event. allow researcher better model temporal dependence binary time-series cross-section (\"BTSCS\") models. improvement sbtscs() (included package) ability flexibly work data lots NAs bracket observed event data. used peacesciencer package.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ","text":"","code":"ps_btscs(data, event, tvar, csunit, pad_ts = FALSE)"},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ","text":"data data set working event event (0, 1) want spells peace years tvar time variable (e.g. year) csunit cross-sectional unit (likely dyad boilerplate international conflict stuff) pad_ts time-series filled panels unbalanced/gaps? Defaults FALSE.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ","text":"ps_btscs() takes data frame returns data frame new variable named spell.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ","text":"function derived sbtscs(). See documentation information.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create ","text":"Armstrong, Dave. 2016. “DAMisc: Dave Armstrong's Miscellaneous Functions.” R package version 1.4-3. Miller, Steven V. 2017. “Quickly Create Peace Years BTSCS Models sbtscs stevemisc.” http://svmiller.com/blog/2017/06/quickly-create-peace-years--btscs-models--stevemisc/","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create ","text":"David . Armstrong, Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ","text":"","code":"# \\donttest{ library(dplyr) library(stevemisc) data(usa_mids) # notice: no quotes ps_btscs(usa_mids, midongoing, year, dyad) #> Joining with `by = join_by(dyad, year)` #> # A tibble: 14,586 × 7 #> dyad ccode1 ccode2 year midongoing midonset spell #> #> 1 1002020 2 20 1920 0 0 0 #> 2 1002020 2 20 1921 0 0 1 #> 3 1002020 2 20 1922 0 0 2 #> 4 1002020 2 20 1923 0 0 3 #> 5 1002020 2 20 1924 0 0 4 #> 6 1002020 2 20 1925 0 0 5 #> 7 1002020 2 20 1926 0 0 6 #> 8 1002020 2 20 1927 0 0 7 #> 9 1002020 2 20 1928 0 0 8 #> 10 1002020 2 20 1929 0 0 9 #> # ℹ 14,576 more rows # }"},{"path":"http://svmiller.com/reference/ps_spells.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ","title":"Create ","text":"ps_spells() allows create spells (\"peace years\" international conflict context) observations event. allow researcher better model temporal dependence binary time-series cross-section (\"BTSCS\") models. function one three package, contents function partly ported add_duration() function spduration package. function, unlike two offer , works much better panels decidedly imbalanced.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ","text":"","code":"ps_spells(data, event, tvar, csunit, time_type = \"year\", ongoing = FALSE)"},{"path":"http://svmiller.com/reference/ps_spells.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ","text":"data data set working event event (0, 1) want spells tvar time variable (e.g. year) csunit cross-sectional unit (e.g. dyad leader) time_type type time-unit data? Right now, work years support months days forthcoming. anything argument just yet. ongoing TRUE, successive 1s considered ongoing events treated NA first 1. FALSE, successive 1s treated failures. Defaults FALSE.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ","text":"ps_spells() takes data frame returns data frame new variable named spell.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ","text":"function derived add_duration() spduration package. See documentation information. thank Andreas Beger blessing port parts .","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create ","text":"Beger, Andreas, Daina Chiba, Daniel W. Hill, Jr, Nils W. Metternich, Shahryar Minhas Michael D. Ward. 2018. “spduration: Split-Population Duration (Cure) Regression.” R package version 0.17.1.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create ","text":"Andreas Beger, Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ","text":"","code":"One <- ps_btscs(usa_mids, midongoing, year, dyad) #> Joining with `by = join_by(dyad, year)` Two <- ps_spells(usa_mids, midongoing, year, dyad) #> Joining with `by = join_by(orig_order)` identical(One, Two) #> [1] TRUE"},{"path":"http://svmiller.com/reference/r1sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale a vector by one standard deviation — r1sd","title":"Scale a vector by one standard deviation — r1sd","text":"r1sd allows rescale numeric vector ensuing output mean 0 standard deviation 1.","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale a vector by one standard deviation — r1sd","text":"","code":"r1sd(x, na = TRUE)"},{"path":"http://svmiller.com/reference/r1sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scale a vector by one standard deviation — r1sd","text":"x numeric vector na NAs vector. Defaults TRUE (.e. passes missing observations)","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scale a vector by one standard deviation — r1sd","text":"function returns numeric vector rescaled mean 0 standard deviation 1.","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale a vector by one standard deviation — r1sd","text":"convenience function since default rescale() function additional weirdness welcome use cases. default, na.rm set TRUE.","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale a vector by one standard deviation — r1sd","text":"","code":"x <- rnorm(100) r1sd(x) #> [1] 0.8589857352 0.6882792081 -0.3820161132 -0.2185607108 -2.3581866933 #> [6] -1.1186076412 -0.9259636957 0.3993068214 -0.9705208235 0.9448918531 #> [11] -1.9134339921 0.5798526209 0.6713547170 0.2153457412 1.3869096436 #> [16] 0.3956947758 -0.4787230015 0.4141902533 -0.8131107521 -0.9124984890 #> [21] 1.7734287396 1.3392256447 0.1729591211 -1.4489497087 -0.0413043116 #> [26] 0.0220676683 -2.2579591760 -0.7365099747 -1.7214374886 0.9269118133 #> [31] 0.2402823685 -1.5983065925 0.1540897909 0.4292600416 0.5623382525 #> [36] 0.9417246305 0.7862868975 0.4378974028 0.5348330059 1.3801393606 #> [41] -0.0601846493 0.0949865211 1.2914664780 -0.0007232612 0.9247860528 #> [46] -0.1117526441 -0.5317138969 -0.1530381957 0.8195438764 -1.0799893600 #> [51] 0.2095907871 -0.2415049796 2.6475450451 -1.0827106024 -0.3501653160 #> [56] 0.2515200939 1.9588504804 -0.2365637631 0.6123825135 -1.9958963302 #> [61] 0.3751642922 0.9637732620 0.4935186982 0.1102981212 -0.0458281813 #> [66] 0.0081144400 -2.4718601906 0.3977882087 1.1481818390 1.4765321954 #> [71] -0.4463775814 1.2445406548 -0.7944924161 -1.4013223523 1.2760452552 #> [76] -0.5366643254 0.0709421095 -1.0050927198 1.3207408527 1.0405286073 #> [81] 0.4652549313 -0.0473292065 -0.6480720990 0.9625997253 -1.4998238910 #> [86] -0.8196351690 -1.2349042335 0.8690667709 -0.6636137487 -0.9215064628 #> [91] -0.2838803156 -0.1382895314 -0.1508800263 1.0792471933 -0.8381494592 #> [96] 0.5004442955 -0.0965475607 -1.6511651186 0.5083293670 0.0577279770"},{"path":"http://svmiller.com/reference/r2sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale a vector (or vectors) by two standard deviations — r2sd","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"r2sd allows rescale numeric vector ensuing output mean 0 standard deviation .5. r2sd_at wrapper mutate_at rename_at dplyr. rescales supplied vectors new vectors renames vectors prefix z_.","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"","code":"r2sd(x, na = TRUE)"},{"path":"http://svmiller.com/reference/r2sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"x vector, likely data frame na NAs vector. Defaults TRUE (.e. passes missing observations)","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"function returns numeric vector rescaled mean 0 standard deviation .5.","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"default, na.rm set TRUE. missing data, function just pass . Gelman (2008) argues rescaling two standard deviations puts regression inputs roughly scale matter original scale. allows honest, preliminary, assessment relative effect sizes regression output. , without requiring rescale function arm. trying reduce packages workflow relies. Importantly, tend rescale ordinal interval inputs leave binary inputs 0/1. , r2sd function fancier -else statements Gelman's rescale function .","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"Gelman, Andrew. 2008. \"Scaling Regression Inputs Dividing Two Standard Deviations.\" Statistics Medicine 27: 2865–2873.","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"","code":"x <- rnorm(100) r2sd(x) #> [1] 0.58938435 0.18197583 0.58039130 0.91074730 0.01668217 -0.18478770 #> [7] -0.01962033 -0.42179684 0.36908293 0.06542483 -0.88627356 0.28892837 #> [13] -0.98614536 0.50553860 -0.51143024 -0.23288148 0.31638425 -0.16144326 #> [19] -0.07254027 1.07923937 1.02523837 0.21173516 0.01595284 0.21346861 #> [25] -0.36107322 -0.05857578 -1.27614690 -0.35787795 -0.22397223 0.15244818 #> [31] -0.23264380 0.95027701 -0.03677793 -0.54346030 0.43066237 -0.39212414 #> [37] -0.58999446 0.58785329 -0.08272961 -0.05357957 0.17797965 0.60034952 #> [43] -0.25598906 0.04699418 0.15419367 0.58142698 0.74035987 0.56813722 #> [49] -0.09961741 0.03155173 0.36507027 0.24989286 0.13771068 -0.08150362 #> [55] 0.40591857 -0.22073909 1.06251295 -0.14558226 -0.53391304 -0.61590666 #> [61] 0.03739685 0.00918478 -0.24785673 -0.34481666 0.39803186 -1.28439892 #> [67] 0.42355915 -0.14919976 0.53538693 0.43795067 0.25980651 0.23653250 #> [73] -0.31081525 -0.53525793 -0.02476267 0.27148311 -1.27299036 -0.55505474 #> [79] -0.67254477 -0.12399040 -1.14078240 -0.22691123 -0.64409311 0.43743081 #> [85] 0.15814365 -0.09573691 -0.19889374 -0.21807596 0.06221853 0.08736401 #> [91] 0.59476649 -0.18209050 0.70475613 0.33387888 0.25281224 -0.22382176 #> [97] 0.29469543 0.21196985 -0.64322852 -0.62643329 r2sd_at(mtcars, c(\"mpg\", \"hp\", \"disp\")) #> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> z_mpg z_hp z_disp #> Mazda RX4 0.07544241 -0.26754642 -0.28530991 #> Mazda RX4 Wag 0.07544241 -0.26754642 -0.28530991 #> Datsun 710 0.22477172 -0.39152023 -0.49509105 #> Hornet 4 Drive 0.10862670 -0.26754642 0.11004685 #> Hornet Sportabout -0.11536726 0.20647109 0.52154061 #> Valiant -0.16514370 -0.30400931 -0.02308349 #> Duster 360 -0.48039447 0.71695148 0.52154061 #> Merc 240D 0.35750889 -0.61759012 -0.33896547 #> Merc 230 0.22477172 -0.37693508 -0.36276756 #> Merc 280 -0.07388690 -0.17274292 -0.25464959 #> Merc 280C -0.19003192 -0.17274292 -0.25464959 #> Merc 450SE -0.30617694 0.24293397 0.18185654 #> Merc 450SL -0.23151228 0.24293397 0.18185654 #> Merc 450SLC -0.40572981 0.24293397 0.18185654 #> Cadillac Fleetwood -0.80394131 0.42524840 0.97337691 #> Lincoln Continental -0.80394131 0.49817417 0.92496588 #> Chrysler Imperial -0.44721018 0.60756282 0.84428082 #> Fiat 128 1.02119472 -0.58841981 -0.61329465 #> Honda Civic 0.85527326 -0.69051589 -0.62539740 #> Toyota Corolla 1.14563581 -0.59571239 -0.64395497 #> Toyota Corona 0.11692278 -0.36234992 -0.44627659 #> Dodge Challenger -0.38084159 0.02415666 0.35210200 #> AMC Javelin -0.40572981 0.02415666 0.29562247 #> Camaro Z28 -0.56335520 0.71695148 0.48119809 #> Pontiac Firebird -0.07388690 0.20647109 0.68291072 #> Fiat X1-9 0.59809500 -0.58841981 -0.61208437 #> Porsche 914-2 0.49024605 -0.40610538 -0.44546974 #> Lotus Europa 0.85527326 -0.24566869 -0.54713290 #> Ford Pantera L -0.35595337 0.85551044 0.48523234 #> Ferrari Dino -0.03240653 0.20647109 -0.34582370 #> Maserati Bora -0.42232196 1.37328341 0.28351971 #> Volvo 142E 0.10862670 -0.27483900 -0.44264576"},{"path":"http://svmiller.com/reference/rbnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"rbnorm() function randomly generate values bounded normal (really: scaled beta) distribution specified mean, standard deviation, upper/lower bounds. use function randomly generate data treat interval sake getting means standard deviations, discernible bounds (even skew) teach students things like random sampling central limit theorem.","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"","code":"rbnorm(n, mean, sd, lowerbound, upperbound, round = FALSE, seed)"},{"path":"http://svmiller.com/reference/rbnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"n number observations simulate mean mean approximate sd standard deviation approximate lowerbound lower bound data generated upperbound upper bound data generated round whether round values whole integers. Defaults FALSE seed set optional seed","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"function returns vector simulated data approximating user-specified conditions.","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"call \"bounded normal\" really beta distribution. aware . took much code somewhere. forget .","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"","code":"library(tibble) tibble(x = rbnorm(10000, 57, 14, 0, 100)) #> # A tibble: 10,000 × 1 #> x #> #> 1 51.3 #> 2 75.9 #> 3 57.1 #> 4 66.4 #> 5 34.2 #> 6 71.4 #> 7 29.8 #> 8 42.5 #> 9 66.2 #> 10 56.2 #> # ℹ 9,990 more rows tibble(x = rbnorm(10000, 57, 14, 0, 100, round = TRUE)) #> # A tibble: 10,000 × 1 #> x #> #> 1 58 #> 2 27 #> 3 56 #> 4 53 #> 5 49 #> 6 80 #> 7 47 #> 8 74 #> 9 61 #> 10 76 #> # ℹ 9,990 more rows tibble(x = rbnorm(10000, 57, 14, 0, 100, seed = 8675309)) #> # A tibble: 10,000 × 1 #> x #> #> 1 72.8 #> 2 44.6 #> 3 66.9 #> 4 38.4 #> 5 39.8 #> 6 56.5 #> 7 45.5 #> 8 47.3 #> 9 41.4 #> 10 39.2 #> # ℹ 9,990 more rows"},{"path":"http://svmiller.com/reference/rd_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual Density Plot for Linear Models — rd_plot","title":"Residual Density Plot for Linear Models — rd_plot","text":"rd_plot() provides visual diagnostic normality assumption linear model. Provided OLS model fit lm() base R, function extracts residuals model creates density plot residuals (solid black line) standard normal distribution mean 0 standard deviation matching standard deviation residuals model. function may used diagnostic purposes.","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual Density Plot for Linear Models — rd_plot","text":"","code":"rd_plot(mod)"},{"path":"http://svmiller.com/reference/rd_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual Density Plot for Linear Models — rd_plot","text":"mod fitted linear model","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual Density Plot for Linear Models — rd_plot","text":"rd_plot() returns density plot ggplot2 object. density plot actual residuals solid black line. stylized normal distribution matching description residuals blue dashed line.","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Residual Density Plot for Linear Models — rd_plot","text":"user can always add ggplot2 elements top greater legibility/clarity. example, density plots can finicky making observations appear . Perhaps adjusting scale x ad hoc, fact, may warranted. goal function emphasize many real world applications, normality assumption residuals never held can often reasonably approximated upon visual inspection.","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Residual Density Plot for Linear Models — rd_plot","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Residual Density Plot for Linear Models — rd_plot","text":"","code":"M1 <- lm(mpg ~ ., data=mtcars) rd_plot(M1)"},{"path":"http://svmiller.com/reference/revcode.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse code a numeric variable — revcode","title":"Reverse code a numeric variable — revcode","text":"revcode allows reverse code numeric variable. , say, Likert item values 1, 2, 3, 4, 5, function inverts scale 1 = 5, 2 = 4, 3 = 3, 4 = 2, 5 = 1.","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse code a numeric variable — revcode","text":"","code":"revcode(x)"},{"path":"http://svmiller.com/reference/revcode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse code a numeric variable — revcode","text":"x numeric vector","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse code a numeric variable — revcode","text":"function returns numeric vector reverse codes numeric vector supplied .","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reverse code a numeric variable — revcode","text":"function passes NAs may variable. assume, reasonably might add, observed values include minimum maximum. usually case discrete ordered-categorical variable (like Likert item). also assumes numeric vector supplied contains possible values minimum observed value 1. usually safe assumption survey data variable interest ordinal (either 1:4 scale, 1:5 scale, 1:10 scale). matter, use function mind.","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse code a numeric variable — revcode","text":"","code":"data.frame(x1 = rep(c(1:7, NA), 2), x2 = c(1:10, 1:4, NA, NA), x3 = rep(c(1:4), 4)) -> example_data library(dplyr) library(magrittr) example_data %>% mutate_at(vars(\"x1\", \"x2\", \"x3\"), ~revcode(.)) #> x1 x2 x3 #> 1 7 10 4 #> 2 6 9 3 #> 3 5 8 2 #> 4 4 7 1 #> 5 3 6 4 #> 6 2 5 3 #> 7 1 4 2 #> 8 NA 3 1 #> 9 7 2 4 #> 10 6 1 3 #> 11 5 10 2 #> 12 4 9 1 #> 13 3 8 4 #> 14 2 7 3 #> 15 1 NA 2 #> 16 NA NA 1"},{"path":"http://svmiller.com/reference/sbayesboot.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"sbayesboot() performs Bayesian bootstrap regression model.","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"","code":"sbayesboot(object, reps = 1000L, seed, cluster = NULL, ...)"},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"object regression model object reps many bootstrap replicates user wants. Defaults 1000 seed set optional seed reproducibility cluster optional cluster calibrating weights ... optional arguments","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"sbayesboot() takes fitted regression model returns matrix bootstrapped coefficients (intercept). easily converted data frame ease summary.","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"code underpinning sbayesboot() largely derived code provided Grant McDermott Vincent Arel-Bundock. approach takes flexibility McDermott's model-agnostic code (along ease specifying clusters) combines Arel-Bundock's update() approach actual bootstrapping. may screwed something , feel free point cases screw .","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"Grant McDermott, Vincent Arel-Bundock","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"","code":"# \\donttest{ M1 <- lm(mpg ~ disp + wt + hp, mtcars) # Default options BB1 <- sbayesboot(M1) # Cluster bootstrap on cylinder variable BB2 <- sbayesboot(M1, cluster=~cyl) # }"},{"path":"http://svmiller.com/reference/sbtscs.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ","title":"Create ","text":"sbtscs() allows create spells (\"peace years\" international conflict context) observations event. allow researcher better model temporal dependence binary time-series cross-section (\"BTSCS\") models.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ","text":"","code":"sbtscs(data, event, tvar, csunit, pad_ts = FALSE)"},{"path":"http://svmiller.com/reference/sbtscs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ","text":"data data set working event event (0, 1) want spells peace years tvar time variable (e.g. year) csunit cross-sectional unit (likely dyad boilerplate international conflict stuff) pad_ts time-series filled panels unbalanced/gaps? Defaults FALSE.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ","text":"sbtscs() takes data frame returns data frame new variable named spell.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ","text":"confess outright, obvious anyone looks code, liberally copy Dave Armstrong's btscs() function DAMisc package. offer two improvements. One, btscs() function chokes large number cross-sectional units recorded \"event.\" know happens . , \"tidying\" code leaning dplyr substantially speeds computation. Incidentally, concerns cross-sectional units recorded events can choke btscs() function large numbers.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create ","text":"Armstrong, Dave. 2016. “DAMisc: Dave Armstrong's Miscellaneous Functions.” R package version 1.4-3. Miller, Steven V. 2017. “Quickly Create Peace Years BTSCS Models sbtscs stevemisc.” http://svmiller.com/blog/2017/06/quickly-create-peace-years--btscs-models--stevemisc/","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create ","text":"David . Armstrong, Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ","text":"","code":"if (FALSE) { # \\dontrun{ library(dplyr) library(stevemisc) data(usa_mids) # notice: no quotes sbtscs(usa_mids, midongoing, year, dyad) } # }"},{"path":"http://svmiller.com/reference/show_ranef.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a caterpillar plot of random effects from a mixed model — show_ranef","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"show_ranef() allows user estimating mixed model quickly plot random intercepts (conditional variances) given random effect mixed model. cases random slope intercept, function plots random slope another caterpillar plot (another facet)","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"","code":"show_ranef(model, group, reorder = TRUE)"},{"path":"http://svmiller.com/reference/show_ranef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"model fitted mixed model random intercepts group random intercept/slopes want see caterpillar plot? Declare character reorder optional argument. DEFAULT TRUE, “re-orders” intercepts original value data. FALSE, ensuing caterpillar plot defaults default method ordering levels random effect estimated conditional mode.","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"show_ranef() returns caterpillar plot random intercepts given mixed model. broom.mixed::augment() can process , function work just fine.","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"function simple wrapper broom.mixed , obviously ggplot2 heavy lifting.","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"","code":"library(lme4) #> Loading required package: Matrix library(stevemisc) data(sleepstudy) M1 <- lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy) show_ranef(M1, \"Subject\") show_ranef(M1, \"Subject\", reorder=FALSE)"},{"path":"http://svmiller.com/reference/smvrnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate from a Multivariate Normal Distribution — smvrnorm","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"smvrnorm() simulates data multivariate normal distribution.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"","code":"smvrnorm( n = 1, mu, sigma, tol = 1e-06, empirical = FALSE, eispack = FALSE, seed )"},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"n number observations simulate mu vector means sigma positive-definite symmetric matrix specifying covariance matrix variables. tol tolerance (relative largest variance) numerical lack positive-definiteness sigma. empirical logical. true, mu sigma specify empirical population mean covariance matrix. eispack logical. values FALSE result error seed set optional seed","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"function returns simulated data multivariate normal distribution.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"simple port rename mvrnorm() MASS package. elect plagiarize/port MASS package conflicts lot things workflow, especially select(). useful \"informal Bayes\" approaches generating quantities interest regression model.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"B. D. Ripley (1987) Stochastic Simulation. Wiley. Page 98.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"","code":"M1 <- lm(mpg ~ disp + cyl, mtcars) smvrnorm(100, coef(M1), vcov(M1)) #> (Intercept) disp cyl #> [1,] 34.44180 -0.016093590 -1.6668364 #> [2,] 32.03398 -0.018701730 -1.2757296 #> [3,] 37.36271 -0.020331480 -2.0825009 #> [4,] 36.71988 -0.012678111 -2.3042494 #> [5,] 34.98588 -0.030837751 -1.2148439 #> [6,] 35.75183 -0.031857450 -1.3338967 #> [7,] 36.17367 -0.006791696 -2.3472860 #> [8,] 32.91489 -0.026496439 -1.0494384 #> [9,] 36.29545 -0.026003203 -1.5875640 #> [10,] 34.96627 -0.016327081 -1.7916997 #> [11,] 30.70833 -0.034137268 -0.4632021 #> [12,] 35.25495 -0.024852183 -1.5871001 #> [13,] 29.67764 -0.045307263 0.1279449 #> [14,] 33.27457 -0.024805958 -1.2678062 #> [15,] 35.90700 -0.028802646 -1.3244104 #> [16,] 32.70040 -0.028711966 -0.8978397 #> [17,] 35.64675 -0.007246586 -2.2356900 #> [18,] 35.50407 -0.023854934 -1.6782045 #> [19,] 33.96691 -0.030668985 -1.2353990 #> [20,] 37.86235 -0.004940957 -2.7436385 #> [21,] 33.28914 -0.012421275 -1.7446521 #> [22,] 34.30010 -0.028712418 -1.3577551 #> [23,] 36.06222 -0.021116329 -1.8633183 #> [24,] 37.46621 -0.023343183 -1.8936744 #> [25,] 36.07328 -0.021921595 -1.8317841 #> [26,] 36.56082 -0.019457823 -1.8880890 #> [27,] 31.39368 -0.040165534 -0.4286164 #> [28,] 29.33698 -0.032083182 -0.4472746 #> [29,] 34.28392 -0.013035185 -1.8310718 #> [30,] 33.58597 -0.023962357 -1.3969869 #> [31,] 28.97209 -0.030077306 -0.3020914 #> [32,] 34.96147 -0.001392552 -2.3925628 #> [33,] 34.29185 -0.027030451 -1.3731181 #> [34,] 30.42862 -0.020690303 -0.8481803 #> [35,] 36.42266 -0.017422488 -1.9164967 #> [36,] 33.56155 -0.043038754 -0.5721694 #> [37,] 31.87355 -0.019477538 -1.2724780 #> [38,] 36.74445 -0.016871084 -1.9450795 #> [39,] 32.78354 -0.016504146 -1.5442419 #> [40,] 35.39997 -0.019395674 -1.8751268 #> [41,] 35.60957 -0.020148263 -1.6666933 #> [42,] 37.99107 0.007172362 -3.3032306 #> [43,] 40.85089 -0.010726402 -2.9515661 #> [44,] 37.95101 -0.028593076 -1.7685981 #> [45,] 33.67591 -0.022775762 -1.3301353 #> [46,] 37.97592 -0.015515732 -2.4204318 #> [47,] 33.96554 -0.019441511 -1.6081523 #> [48,] 34.18454 -0.019333806 -1.4407924 #> [49,] 34.55656 -0.025640883 -1.3469341 #> [50,] 33.92344 -0.014969830 -1.6643016 #> [51,] 34.34051 -0.020552987 -1.5917340 #> [52,] 34.10286 -0.032555955 -1.0019450 #> [53,] 35.99606 -0.016518191 -1.8187662 #> [54,] 31.72513 -0.029475953 -0.7907692 #> [55,] 32.81959 -0.022554931 -1.2138406 #> [56,] 35.99504 -0.028737140 -1.5838799 #> [57,] 33.62026 -0.012538926 -1.6514403 #> [58,] 37.26592 -0.022992995 -1.8182772 #> [59,] 36.14499 -0.017645647 -1.9345679 #> [60,] 35.23342 -0.004586844 -2.2137780 #> [61,] 31.81256 -0.043409009 -0.2694654 #> [62,] 36.43215 -0.022729756 -1.8196314 #> [63,] 39.28006 -0.004245869 -2.9370557 #> [64,] 34.91233 -0.032243438 -1.1931419 #> [65,] 31.46309 -0.015660756 -1.1197044 #> [66,] 34.22477 -0.025875124 -1.3415898 #> [67,] 37.68134 -0.027979933 -1.7807292 #> [68,] 33.35864 -0.017707105 -1.4843595 #> [69,] 33.76621 -0.021178795 -1.5660815 #> [70,] 38.65470 -0.020472447 -2.1995093 #> [71,] 30.48207 -0.028090660 -0.6645297 #> [72,] 32.25134 -0.034734893 -0.6216782 #> [73,] 30.90073 -0.028385834 -0.7434711 #> [74,] 33.09523 -0.033242923 -0.8108385 #> [75,] 35.14176 -0.019699942 -1.5724585 #> [76,] 37.28685 -0.012467155 -2.4016195 #> [77,] 36.72132 -0.008554207 -2.4195983 #> [78,] 36.02494 -0.024415962 -1.8528437 #> [79,] 34.11541 -0.031099366 -1.2083065 #> [80,] 34.19654 -0.020677674 -1.4154843 #> [81,] 30.07842 -0.025284376 -0.6767066 #> [82,] 37.29116 -0.018444192 -2.3045275 #> [83,] 38.45071 -0.010092197 -2.5877892 #> [84,] 33.82277 -0.023651190 -1.3520588 #> [85,] 34.66278 -0.014667477 -1.7998157 #> [86,] 31.40191 -0.032412551 -0.7281680 #> [87,] 42.25891 -0.009559488 -3.1434194 #> [88,] 34.35595 -0.006480486 -2.1190544 #> [89,] 36.11058 -0.010001272 -2.2122046 #> [90,] 33.28499 -0.012173669 -1.6930537 #> [91,] 34.92931 -0.018505094 -1.5643630 #> [92,] 35.37642 -0.025342419 -1.5472113 #> [93,] 35.41801 -0.013244018 -1.9558777 #> [94,] 36.00575 -0.022021632 -1.7105708 #> [95,] 33.56557 -0.031543846 -0.9619895 #> [96,] 34.27518 -0.026714387 -1.2335824 #> [97,] 36.98794 -0.003431031 -2.5075174 #> [98,] 38.92558 -0.018050955 -2.3751259 #> [99,] 37.27147 -0.015998570 -2.1360520 #> [100,] 30.62294 -0.033973310 -0.4645830"},{"path":"http://svmiller.com/reference/stevepubs.html","id":null,"dir":"Reference","previous_headings":"","what":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"data publications, barring things like book reviews forthcoming pieces. use data illustrate print_refs() function. cite publications .","code":""},{"path":"http://svmiller.com/reference/stevepubs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"","code":"stevepubs"},{"path":"http://svmiller.com/reference/stevepubs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"data frame following 14 variables. CATEGORY entry type BIBTEXKEY unique entry key AUTHOR list authors entry BOOKTITLE book title, appropriate JOURNAL journal title, appropriate NUMBER journal volume number, appropriate PAGES range page numbers, appropriate PUBLISHER book publisher, appropriate TITLE title publication VOLUME journal volume number, appropriate YEAR year publication, character. Publications year assumed forthcoming DOI DOI, entered one","code":""},{"path":"http://svmiller.com/reference/stevepubs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"Cite publications , goons. Extremely Smokey Bear voice can jack h-index infinity.","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":null,"dir":"Reference","previous_headings":"","what":"Strategic Rivalries, 1494-2010 — strategic_rivalries","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"simple summary strategic (inter-state) rivalries Thompson Dreyer (2012).","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"","code":"data(\"strategic_rivalries\")"},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"data frame 197 observations following 10 variables. rivalryno numeric vector rivalry number rivalryname character vector rivalry name sidea character vector first country rivalry sideb character vector second country rivalry styear numeric vector start year rivalry endyear numeric vector end year rivalry region character vector region rivalry, per Thompson Dreyer (2012) type1 character vector primary type rivalry (spatial, positional, ideological, interventionary) type2 character vector secondary type rivalry, applicable (spatial, positional, ideological, interventionary) type3 character vector tertiary type rivalry, applicable (spatial, positional, ideological, interventionary)","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"Information gathered appendix Thompson Dreyer (2012). Ongoing rivalries right-bound 2010, date publication Thompson Dreyer's handbook. Users free change like.","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"Thompson, William R. David Dreyer. 2012. Handbook International Rivalries. CQ Press.","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"","code":"data(strategic_rivalries)"},{"path":"http://svmiller.com/reference/studentt.html","id":null,"dir":"Reference","previous_headings":"","what":"The Student-t Distribution (Location-Scale) — studentt","title":"The Student-t Distribution (Location-Scale) — studentt","text":"density, distribution function, quantile function random generation Student-t distribution location mu, scale sigma, degrees freedom df. Base R gives -called \"standard\" Student-t distribution, just varying degrees freedom. generalizes standard Student-t three-parameter version.","code":""},{"path":"http://svmiller.com/reference/studentt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Student-t Distribution (Location-Scale) — studentt","text":"","code":"dst(x, df, mu, sigma) pst(q, df, mu, sigma) qst(p, df, mu, sigma) rst(n, df, mu, sigma)"},{"path":"http://svmiller.com/reference/studentt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Student-t Distribution (Location-Scale) — studentt","text":"x, q vector quantiles df vector degrees freedom mu vector location value sigma vector scale values p Vector probabilities. n Number samples draw distribution.","code":""},{"path":"http://svmiller.com/reference/studentt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Student-t Distribution (Location-Scale) — studentt","text":"dst() returns density. pst() returns distribution function. qst() returns quantile function. rst() returns random numbers.","code":""},{"path":"http://svmiller.com/reference/studentt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Student-t Distribution (Location-Scale) — studentt","text":"simple hack taken Wikipedia. itch wanting scratch . can probably generalize outward allow tail log stuff, wrote mostly random number generation. Right now, written account fact sigma non-negative, user know (now).","code":""},{"path":[]},{"path":"http://svmiller.com/reference/tbl_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data frame to an object of class ","title":"Convert data frame to an object of class ","text":"tbl_df() ensures legacy compatibility scripts since function deprecated dplyr. to_tbl() also added fun.","code":""},{"path":"http://svmiller.com/reference/tbl_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data frame to an object of class ","text":"","code":"tbl_df(...) to_tbl(...)"},{"path":"http://svmiller.com/reference/tbl_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data frame to an object of class ","text":"... optional parameters, put anything . just quell CRAN checks.","code":""},{"path":"http://svmiller.com/reference/tbl_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data frame to an object of class ","text":"function takes data frame turns tibble.","code":""},{"path":"http://svmiller.com/reference/tbl_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data frame to an object of class ","text":"","code":"tbl_df(mtcars) #> Warning: `tbl_df()` was deprecated in dplyr 1.0.0. #> ℹ Please use `tibble::as_tibble()` instead. #> # A tibble: 32 × 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ℹ 22 more rows tbl_df(iris) #> Warning: `tbl_df()` was deprecated in dplyr 1.0.0. #> ℹ Please use `tibble::as_tibble()` instead. #> # A tibble: 150 × 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # ℹ 140 more rows"},{"path":"http://svmiller.com/reference/usa_mids.html","id":null,"dir":"Reference","previous_headings":"","what":"United States Militarized Interstate Disputes (MIDs) — usa_mids","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"non-directed dyad-year data set militarized interstate disputes involving United States. created illustrate sbtscs() function.","code":""},{"path":"http://svmiller.com/reference/usa_mids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"","code":"usa_mids"},{"path":"http://svmiller.com/reference/usa_mids.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"data frame 14586 observations following 6 variables. dyad unique identifier dyad ccode1 Correlates War state code United States (2) ccode2 Correlates War state code state dyad year observation year dyad midongoing ongoing inter-state dispute dyad-year? midonset new inter-state dispute onset dyad-year","code":""},{"path":"http://svmiller.com/reference/usa_mids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"Data generated time ago. Rare cases multiple disputes ongoing given dyad-year first whittled isolating 1) unique dispute onsets. Thereafter, data select 2) highest fatality, 3) highest hostility level, 4) longer dispute, 5) just picking whichever one came first. duplicate non-directed dyad-year observations.","code":""},{"path":"http://svmiller.com/reference/usa_mids.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"Gibler, Douglas M., Steven V. Miller, Erin K. Little. 2016. “Analysis Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.","code":""},{"path":"http://svmiller.com/reference/wls.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Weighted Least Squares of Your OLS Model — wls","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"wls() takes OLS model re-estimates using weighted least squares approach. Weighted least squares often \"textbook\" approach dealing presence heteroskedastic standard errors, weighted least squares estimates compared OLS estimates uncertainty check consistency potential inferential implications.","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"","code":"wls(mod)"},{"path":"http://svmiller.com/reference/wls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"mod fitted OLS model","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"wls() returns new model object weighted least squares re-estimation OLS model supplied .","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"function robust potential model specification oddities (e.g. polynomials fixed effects). also perform nicely presence missing data, na.action = na.exclude supplied first offending OLS model supplied function weighted least squares re-estimation.","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"","code":"M1 <- lm(mpg ~ ., data=mtcars) M2 <- wls(M1) summary(M2) #> #> Call: #> lm(formula = mpg ~ cyl + disp + hp + drat + wt + qsec + vs + #> am + gear + carb, data = A, weights = wts) #> #> Weighted Residuals: #> Min 1Q Median 3Q Max #> -1.7522 -0.8385 -0.2326 0.9062 2.7257 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 16.798050 19.371537 0.867 0.396 #> cyl -0.394419 1.021128 -0.386 0.703 #> disp 0.002299 0.015834 0.145 0.886 #> hp -0.009795 0.020927 -0.468 0.645 #> drat 0.934691 1.646690 0.568 0.576 #> wt -2.383075 1.748083 -1.363 0.187 #> qsec 0.552577 0.760879 0.726 0.476 #> vs -0.124255 2.231573 -0.056 0.956 #> am 2.236543 2.158780 1.036 0.312 #> gear 0.484887 1.520711 0.319 0.753 #> carb -0.564120 0.802962 -0.703 0.490 #> #> Residual standard error: 1.518 on 21 degrees of freedom #> Multiple R-squared: 0.8621,\tAdjusted R-squared: 0.7965 #> F-statistic: 13.13 on 10 and 21 DF, p-value: 6.318e-07 #>"},{"path":"http://svmiller.com/reference/wom.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Week of the Month from a Date — wom","title":"Generate Week of the Month from a Date — wom","text":"wom() convenience function use constructing calendars ggplot2. takes date returns, numeric vector, week month date given .","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Week of the Month from a Date — wom","text":"","code":"wom(x)"},{"path":"http://svmiller.com/reference/wom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Week of the Month from a Date — wom","text":"x date","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Week of the Month from a Date — wom","text":"wom() convenience function use constructing calendars ggplot2. takes date returns, numeric vector, week month date given .","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Week of the Month from a Date — wom","text":"wom() assumes Sunday start week. can assuredly customized later function, right now assumption Sunday start week (Monday, might contexts).","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Week of the Month from a Date — wom","text":"","code":"wom(as.Date(\"2022-01-01\")) #> [1] 1 wom(Sys.Date()) #> [1] 5"},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-190","dir":"Changelog","previous_headings":"","what":"stevemisc 1.9.0","title":"stevemisc 1.9.0","text":"rewb_at() convenience wrapper mean_at(), group_mean_center_at(), center_at(). ’s useful preparing data random effects, within-(REWB) panel analysis. linloess_plot() now special print class suppressing warnings come LOESS smoother. Additionally, suppress_warnings argument function.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-180","dir":"Changelog","previous_headings":"","what":"stevemisc 1.8.0","title":"stevemisc 1.8.0","text":"CRAN release: 2024-08-23 rd_plot() now na.rm = TRUE argument quietly passed extraction standard deviation residuals. ensures missing values data don’t result missing residuals, result standard deviation residuals. linloess_plot() now resid argument allows comparison model’s residuals y-axis rather default (raw values y y-axis). Assorted documentation fixes.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-170","dir":"Changelog","previous_headings":"","what":"stevemisc 1.7.0","title":"stevemisc 1.7.0","text":"CRAN release: 2023-11-06 Add charitable_contributions. Add rd_plot() Scoped helper verbs (“” functions) gradually getting .support , , breaking link superseded _at() functions dplyr. linloess_plot() now se argument optionally disabling standard error bands. particularly ill-fitting linear models, may advisable.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-160","dir":"Changelog","previous_headings":"","what":"stevemisc 1.6.0","title":"stevemisc 1.6.0","text":"CRAN release: 2023-03-22 theme_steve() removed package. function now stevethemes, house ggplot2 themes going forward. Fix warning/error/bug ps_spells() brought attention CRAN. don’t know came just now, ’s apparently issue lurking around R development time now ’s always wrong call order() data frame. underlying order() calls replaced arrange(). fix concerns related issue also affects peacesciencer.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-150","dir":"Changelog","previous_headings":"","what":"stevemisc 1.5.0","title":"stevemisc 1.5.0","text":"CRAN release: 2023-02-01 Package now contains scoped helper verbs—-called “” functions. functions—like center_at(), diff_at(), —self-contained one R Documentation file. theme_steve_ms() now actually uses “Crimson Pro”, “Crimson Text”. theme_steve() deprecated removed later release. function effectively moved stevethemes, also expanded improved. remaining ggplot2 functions package becoming legacy functions mind. wls() weighted least squares re-estimations OLS model. HT @hadley information class issue. fct_reorg() completely re-written (@hadley ) light new forcats release.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-141","dir":"Changelog","previous_headings":"","what":"stevemisc 1.4.1","title":"stevemisc 1.4.1","text":"CRAN release: 2022-04-12 Adjust filter_refs() print_refs() longer require bib2df. , bib2df also removed package dependency.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-140","dir":"Changelog","previous_headings":"","what":"stevemisc 1.4.0","title":"stevemisc 1.4.0","text":"CRAN release: 2022-03-23 Add filter_refs() , , bib2df package dependency. print_refs() now work (implied) bib2df data frame .bib entries. Add wom(). Add sbayesboot(). Add map_quiz. Update stevepubs. Update show_ranef(), longer requires broom.mixed underneath hood. Remove broom.mixed package dependency.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-130","dir":"Changelog","previous_headings":"","what":"stevemisc 1.3.0","title":"stevemisc 1.3.0","text":"CRAN release: 2021-10-22 Add data set French leaders (fra_leaderyears). data set stress-testing peace spell calculations cross-sectional units decidedly imbalanced. Add data set German dyad-years (gmy_dyadyears). data set stress-testing peace spell calculations huge gap data. Add ps_spells(), general spell calculations going forward. Add linloess_plot(). , add tidyr dependency.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-120","dir":"Changelog","previous_headings":"","what":"stevemisc 1.2.0","title":"stevemisc 1.2.0","text":"CRAN release: 2021-07-27 Add prepare_refs() print_refs() Add r2sd_at(). Add revcode(). Add stevepubs. Add theme_steve_ms() theme_steve_font().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-110","dir":"Changelog","previous_headings":"","what":"stevemisc 1.1.0","title":"stevemisc 1.1.0","text":"CRAN release: 2021-06-14 Add ps_btscs() future use peacesciencer. Moved Imports: entries Suggests: CRAN compliance. import packages (DBI, RSQLite, dbplyr) concern db_lselect() function.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-100","dir":"Changelog","previous_headings":"","what":"stevemisc 1.0.0","title":"stevemisc 1.0.0","text":"CRAN release: 2021-04-19 slated first professional/public release CRAN. Package features major updates functions, mostly CRAN compliance. New features include fct_reorg(), gvi() shortcut get_var_info(), ess9_labelled data illustration, scale-location t-distribution functions, .","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-031","dir":"Changelog","previous_headings":"","what":"stevemisc 0.3.1","title":"stevemisc 0.3.1","text":"Move almost data stevedata. Add p_z().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-030","dir":"Changelog","previous_headings":"","what":"stevemisc 0.3.0","title":"stevemisc 0.3.0","text":"Mostly cosmetic fixes functionality things. CRAN compliant.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-022","dir":"Changelog","previous_headings":"","what":"stevemisc 0.2.2","title":"stevemisc 0.2.2","text":"Add usa_mids. Update sbtscs(). Add vignette.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-02","dir":"Changelog","previous_headings":"","what":"stevemisc 0.2","title":"stevemisc 0.2","text":"Update carrec(), cor2data(), corvectors(), get_sims(), get_var_info(), make_perclab(), make_scale(), jenny(), %nin%, normal_dist(), rbnorm(), sbtscs(), show_ranef(), smvrnorm(), theme_steve(), theme_steve_web(). Remove multiplot().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0117","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.17","title":"stevemisc 0.1.17","text":"Update fakeAPI.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0116","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.16","title":"stevemisc 0.1.16","text":"Add seed corvectors(). Add fakeAPI.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0114","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.14","title":"stevemisc 0.1.14","text":"Add corvectors() jenny().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0113","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.13","title":"stevemisc 0.1.13","text":"Add tbl_df() to_tbl(). Update theme_steve_web(). Thanks @mewdewitt suggestions.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0111","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.11","title":"stevemisc 0.1.11","text":"Add %nin%.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0110","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.10","title":"stevemisc 0.1.10","text":"Add smvrnorm().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-018","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.8","title":"stevemisc 0.1.8","text":"Generalize get_sims() handle non-mixed models.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0173","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7.3","title":"stevemisc 0.1.7.3","text":"Update States.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0172","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7.2","title":"stevemisc 0.1.7.2","text":"Update DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0171","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7.1","title":"stevemisc 0.1.7.1","text":"Add seed rbnorm().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-017","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7","title":"stevemisc 0.1.7","text":"Add normal_dist(), States, update Presidents.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0169","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.9","title":"stevemisc 0.1.6.9","text":"Remove Presidents.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0168","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.8","title":"stevemisc 0.1.6.8","text":"Add ESS9GB Presidents.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0166","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.6","title":"stevemisc 0.1.6.6","text":"Add Arca.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0165","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.5","title":"stevemisc 0.1.6.5","text":"Update aluminum_premiums DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0164","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.4","title":"stevemisc 0.1.6.4","text":"Add asn_stats DST.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0162","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.2","title":"stevemisc 0.1.6.2","text":"Add cor2data().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0161","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.1","title":"stevemisc 0.1.6.1","text":"Add select z-values vectors.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01601","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.01","title":"stevemisc 0.1.6.01","text":"Add rbnorm().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0159","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.9","title":"stevemisc 0.1.5.9","text":"Update aluminum_premiums.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0158","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.8","title":"stevemisc 0.1.5.8","text":"Add strategic_rivalries.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0157","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.7","title":"stevemisc 0.1.5.7","text":"Add sugar_prices.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0156","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.6","title":"stevemisc 0.1.5.6","text":"Add post_bg().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0155","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.5","title":"stevemisc 0.1.5.5","text":"Add ghp100k.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0154","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.4","title":"stevemisc 0.1.5.4","text":"Add eustates multiplot().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0152","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.2","title":"stevemisc 0.1.5.2","text":"Add get_sims(). Update theme_steve_web().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0151","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.1","title":"stevemisc 0.1.5.1","text":"Add r2sd().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-015","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5","title":"stevemisc 0.1.5","text":"Add carrec() cardkrieger1994mwe.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01496","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.6","title":"stevemisc 0.1.4.9.6","text":"Add clemsontemps, gss_abortion, map_quiz.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01495","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.5","title":"stevemisc 0.1.4.9.5","text":"Add nesarc_drinkspd.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01493","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.3","title":"stevemisc 0.1.4.9.3","text":"Add usa_chn_gdp_forecasts.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01492","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.2","title":"stevemisc 0.1.4.9.2","text":"Add imf_coffee_data.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01491","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.1","title":"stevemisc 0.1.4.9.1","text":"Add recessions.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0149","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9","title":"stevemisc 0.1.4.9","text":"Add ukg_eeri.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01489","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.9","title":"stevemisc 0.1.4.8.9","text":"Rename edq_passengercars eq_passengercars.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01488","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.8","title":"stevemisc 0.1.4.8.8","text":"Add edq_passengercars.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01487","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.7","title":"stevemisc 0.1.4.8.7","text":"Update documentation migrants_usa mvprod.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01486","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.6","title":"stevemisc 0.1.4.8.6","text":"Add mvprod.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01485","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.5","title":"stevemisc 0.1.4.8.5","text":"Update documentation migrants_usa.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01484","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.4","title":"stevemisc 0.1.4.8.4","text":"Update documentation migrants_usa.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01483","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.3","title":"stevemisc 0.1.4.8.3","text":"Add migrants_usa.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01482","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.2","title":"stevemisc 0.1.4.8.2","text":"Update steve_clothes.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01481","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.1","title":"stevemisc 0.1.4.8.1","text":"Update DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0148","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8","title":"stevemisc 0.1.4.8","text":"Add DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0147","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.7","title":"stevemisc 0.1.4.7","text":"Add aluminum_premiums.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0146","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.6","title":"stevemisc 0.1.4.6","text":"Update theme_steve(), theme_steve_web(), ustradegdp.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0144","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.4","title":"stevemisc 0.1.4.4","text":"Add ustradegdp.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0143","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.3","title":"stevemisc 0.1.4.3","text":"Add steves_clothes.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0142","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.2","title":"stevemisc 0.1.4.2","text":"Add several data sets: articseaice, co2data, osu_results, sealevels.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0141","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.1","title":"stevemisc 0.1.4.1","text":"Fix dplyr NAMESPACE issue,thanks David Armstrong recommending .","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-014","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4","title":"stevemisc 0.1.4","text":"Add get_var_info(), theme_steve_web2(), fonts. inst/fonts directory.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-013","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.3","title":"stevemisc 0.1.3","text":"Add theme_steve_web()","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-012","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.2","title":"stevemisc 0.1.2","text":"Changed title theme_steve(). Add mround2()","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-011","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.1","title":"stevemisc 0.1.1","text":"Changed title theme_steve()","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-010","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.0","title":"stevemisc 0.1.0","text":"Initial developmental release. Features included: sbtscs() show_ranef() theme_steve()","code":""}] +[{"path":"http://svmiller.com/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Steve Miller. Author, maintainer. Ben Bolker. Contributor. Dave Armstrong. Contributor. John Fox. Contributor. Winston Chang. Contributor. Brian Ripley. Contributor. Bill Venables. Contributor. Pascal van Kooten. Contributor. Gerko Vink. Contributor. Paul Williamson. Contributor. Andreas Beger. Contributor. Vincent Arel-Bundock. Contributor. Grant McDermott. Contributor. Hadley Wickham. Contributor.","code":""},{"path":"http://svmiller.com/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Miller S (2025). stevemisc: Steve's Miscellaneous Functions. R package version 1.8.9.","code":"@Manual{, title = {stevemisc: Steve's Miscellaneous Functions}, author = {Steve Miller}, year = {2025}, note = {R package version 1.8.9}, }"},{"path":"http://svmiller.com/index.html","id":"steves-miscellaneous-functions","dir":"","previous_headings":"","what":"Steve's Miscellaneous Functions","title":"Steve's Miscellaneous Functions","text":"{stevemisc} R package includes various functions tools written years assist research, teaching, public presentations (.e. stuff put blog). offer public release 1) vain think want entire, eponymous ecosystem R programming language (.e. “steveverse”) 2) think tools broadly useful users ’m trying bundle things offer (prominently {steveproj}). Namely, {stevemisc} offers tools assist data organization, data presentation, data recoding, data simulation.","code":""},{"path":"http://svmiller.com/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Steve's Miscellaneous Functions","text":"can install CRAN. can install development version {stevemisc} Github via devtools package. suppose using remotes package work well.","code":"install.packages(\"stevemisc\") devtools::install_github(\"svmiller/stevemisc\")"},{"path":"http://svmiller.com/reference/at.html","id":null,"dir":"Reference","previous_headings":"","what":"Scoped Helper Verbs — center_at","title":"Scoped Helper Verbs — center_at","text":"Scoped helper verbs included R Documentation file allow targeted commands specified columns. also rename ensuing output conform preferred style. commands multiple explained details section .","code":""},{"path":"http://svmiller.com/reference/at.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scoped Helper Verbs — center_at","text":"","code":"center_at(data, x, prefix = \"c\", na = TRUE, .by = NULL) diff_at(data, x, o = 1, prefix = \"d\", .by = NULL) group_mean_center_at( data, x, mean_prefix = \"mean\", prefix = \"b\", na = TRUE, .by ) lag_at(data, x, prefix = \"l\", o = 1, .by = NULL) log_at(data, x, prefix = \"ln\", plus_1 = FALSE) mean_at(data, x, prefix = \"mean\", na = TRUE, .by = NULL) r1sd_at(data, x, prefix = \"s\", na = TRUE, .by = NULL) r2sd_at(data, x, prefix = \"z\", na = TRUE, .by = NULL) rewb_at(data, x, w_prefix = \"w\", b_prefix = \"b\", .by)"},{"path":"http://svmiller.com/reference/at.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scoped Helper Verbs — center_at","text":"data data frame x vector, likely data frame prefix Allows user rename prefix new variables. function defaults (see details section). na logical whether missing values ignored creation means re-scaled variables. Defaults TRUE (.e. pass /remove missing observations). applicable diff_at, lag_at, log_at. .selection columns group operation. Defaults NULL. eventually become standard feature functions operator moves beyond experimental dplyr. argument applicable log_at () optional functions except group_mean_center_at. group_mean_center_at must something specified grouped mean-centering. o order lags calculating differences lags diff_at lag_at. Applicable functions. mean_prefix Applicable group_mean_center_at. Specifies prefix (assumed) total population mean variables. Default \"mean\", though user can change see fit. plus_1 Applicable log_at. TRUE, adds 1 variables prior log transformation. FALSE, performs logarithmic transformation variables matter whether 0 occurs (.e. 0s come back -Inf). Defaults FALSE. w_prefix Applicable rewb_at, specifies prefix -called \"within\" variables created procedure. Defaults \"w\". b_prefix Applicable rewb_at, specifies prefix -called \"\" variables created procedure. Defaults \"b\".","code":""},{"path":"http://svmiller.com/reference/at.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scoped Helper Verbs — center_at","text":"function returns set new vectors data frame performing relevant functions. new vectors distinct prefixes corresponding action performed .","code":""},{"path":"http://svmiller.com/reference/at.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scoped Helper Verbs — center_at","text":"center_at wrapper mutate_at rename_at dplyr. takes supplied vectors effectively centers mean. renames new variables prefix c_. default prefix (\"c\") can changed way argument function. diff_at wrapper mutate across dplyr. takes supplied vectors creates differences previous value recorded . renames new variables prefix d_ (case first difference), something like d2_ case second differences, d3_ case third differences (). exact prefix depends o argument, communicates order lags want. defaults 1. default prefix (\"d\") can changed way argument function, though naming convention omit numerical prefix first differences. group_mean_center_at wrapper mutate across dplyr. takes supplied vectors centers (assumed) group mean variables (assumed) total population mean variables provided . returns new variables prefix, whose default b_. prefix communicates, , kind \"\" variable panel model context, juxtaposition \"within\" variables panel model context. lag_at wrapper mutate across dplyr. takes supplied vector(s) creates lag variables . new variables prefix l[o]_ o corresponds order lag (specified argument function, defaults 1). default prefix (\"l\") can changed way another argument function. log_at wrapper mutate across dplyr. takes supplied vectors creates variable takes natural logarithmic transformation . renames new variables prefix ln_. default prefix (\"ln\") can changed way argument function. Users can optionally specify want add 1 vector taking natural logarithm, popular thing positive reals naturally occurring zeroes. mean_at wrapper mutate across dplyr. takes supplied vectors creates variable communicating mean variable. renames new variables prefix mean_. default prefix (\"mean\") can changed way argument function. r1sd_at wrapper mutate across dplyr. rescales supplied vectors new vectors renames vectors prefix s_. Note rescaling just one standard deviation two. default prefix (\"s\") can changed way argument function. r2sd_at wrapper mutate across dplyr. rescales supplied vectors new vectors renames vectors prefix z_. Note rescaling two standard deviations one. default prefix (\"z\") can changed way argument function. rewb_at wrapper routines done mean_at, group_mean_center_at, center_at package. implicitly assumes data panel runs three routines order create -called \"\" \"within\" variables \"random effects, within-\" analysis. Means calculated based available data, na argument available function. function fail presence variables data matching routine wants create. functions, except lag_at, fail absence character vector length one. intended work across multiple columns instead just one. wanting create one new variable, think using dplyr verb .","code":""},{"path":"http://svmiller.com/reference/at.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scoped Helper Verbs — center_at","text":"","code":"set.seed(8675309) Example <- data.frame(category = c(rep(\"A\", 5), rep(\"B\", 5), rep(\"C\", 5)), x = runif(15), y = runif(15), z = sample(1:20, 15, replace=TRUE)) my_vars <- c(\"x\", \"y\", \"z\") center_at(Example, my_vars) #> category x y z c_x c_y c_z #> 1 A 0.1594836 0.91822046 9 -0.45270578 0.38488743 -4 #> 2 A 0.4781883 0.71636154 19 -0.13400109 0.18302851 6 #> 3 A 0.7647987 0.20624914 15 0.15260928 -0.32708389 2 #> 4 A 0.7696877 0.81691683 20 0.15749826 0.28358381 7 #> 5 A 0.2685485 0.71585943 14 -0.34364092 0.18252640 1 #> 6 B 0.6730459 0.06062449 14 0.06085649 -0.47270853 1 #> 7 B 0.9787908 0.84710058 17 0.36660137 0.31376756 4 #> 8 B 0.8463270 0.84676044 8 0.23413755 0.31342741 -5 #> 9 B 0.8566562 0.33261085 16 0.24446673 -0.20072218 3 #> 10 B 0.4451601 0.55965050 16 -0.16702927 0.02631747 3 #> 11 C 0.8382325 0.66946933 12 0.22604312 0.13613631 -1 #> 12 C 0.5833169 0.25463848 18 -0.02887250 -0.27869455 5 #> 13 C 0.5109512 0.07917477 5 -0.10123826 -0.45415826 -8 #> 14 C 0.2601681 0.15996809 6 -0.35202128 -0.37336494 -7 #> 15 C 0.7494857 0.81639049 6 0.13729632 0.28305746 -7 diff_at(Example, my_vars) #> category x y z d_x d_y d_z #> 1 A 0.1594836 0.91822046 9 NA NA NA #> 2 A 0.4781883 0.71636154 19 0.318704692 -0.2018589161 10 #> 3 A 0.7647987 0.20624914 15 0.286610370 -0.5101124048 -4 #> 4 A 0.7696877 0.81691683 20 0.004888979 0.6106676969 5 #> 5 A 0.2685485 0.71585943 14 -0.501139179 -0.1010574063 -6 #> 6 B 0.6730459 0.06062449 14 0.404497413 -0.6552349348 0 #> 7 B 0.9787908 0.84710058 17 0.305744875 0.7864760908 3 #> 8 B 0.8463270 0.84676044 8 -0.132463813 -0.0003401469 -9 #> 9 B 0.8566562 0.33261085 16 0.010329181 -0.5141495881 8 #> 10 B 0.4451601 0.55965050 16 -0.411496004 0.2270396501 0 #> 11 C 0.8382325 0.66946933 12 0.393072386 0.1098188350 -4 #> 12 C 0.5833169 0.25463848 18 -0.254915616 -0.4148308551 6 #> 13 C 0.5109512 0.07917477 5 -0.072365765 -0.1754637090 -13 #> 14 C 0.2601681 0.15996809 6 -0.250783015 0.0807933176 1 #> 15 C 0.7494857 0.81639049 6 0.489317597 0.6564223976 0 diff_at(Example, my_vars, o=3) #> category x y z d3_x d3_y d3_z #> 1 A 0.1594836 0.91822046 9 NA NA NA #> 2 A 0.4781883 0.71636154 19 NA NA NA #> 3 A 0.7647987 0.20624914 15 NA NA NA #> 4 A 0.7696877 0.81691683 20 0.610204041 -0.1013036240 11 #> 5 A 0.2685485 0.71585943 14 -0.209639830 -0.0005021142 -5 #> 6 B 0.6730459 0.06062449 14 -0.091752786 -0.1456246441 -1 #> 7 B 0.9787908 0.84710058 17 0.209103110 0.0301837497 -3 #> 8 B 0.8463270 0.84676044 8 0.577778475 0.1309010091 -6 #> 9 B 0.8566562 0.33261085 16 0.183610243 0.2719863558 2 #> 10 B 0.4451601 0.55965050 16 -0.533630636 -0.2874500849 -1 #> 11 C 0.8382325 0.66946933 12 -0.008094437 -0.1772911029 4 #> 12 C 0.5833169 0.25463848 18 -0.273339234 -0.0779723700 2 #> 13 C 0.5109512 0.07917477 5 0.065791005 -0.4804757291 -11 #> 14 C 0.2601681 0.15996809 6 -0.578064396 -0.5095012465 -6 #> 15 C 0.7494857 0.81639049 6 0.166168816 0.5617520062 -12 lag_at(Example, my_vars) #> category x y z l1_x l1_y l1_z #> 1 A 0.1594836 0.91822046 9 NA NA NA #> 2 A 0.4781883 0.71636154 19 0.1594836 0.91822046 9 #> 3 A 0.7647987 0.20624914 15 0.4781883 0.71636154 19 #> 4 A 0.7696877 0.81691683 20 0.7647987 0.20624914 15 #> 5 A 0.2685485 0.71585943 14 0.7696877 0.81691683 20 #> 6 B 0.6730459 0.06062449 14 0.2685485 0.71585943 14 #> 7 B 0.9787908 0.84710058 17 0.6730459 0.06062449 14 #> 8 B 0.8463270 0.84676044 8 0.9787908 0.84710058 17 #> 9 B 0.8566562 0.33261085 16 0.8463270 0.84676044 8 #> 10 B 0.4451601 0.55965050 16 0.8566562 0.33261085 16 #> 11 C 0.8382325 0.66946933 12 0.4451601 0.55965050 16 #> 12 C 0.5833169 0.25463848 18 0.8382325 0.66946933 12 #> 13 C 0.5109512 0.07917477 5 0.5833169 0.25463848 18 #> 14 C 0.2601681 0.15996809 6 0.5109512 0.07917477 5 #> 15 C 0.7494857 0.81639049 6 0.2601681 0.15996809 6 lag_at(Example, my_vars, o=3) #> category x y z l1_x l2_x l3_x l1_y #> 1 A 0.1594836 0.91822046 9 NA NA NA NA #> 2 A 0.4781883 0.71636154 19 0.1594836 NA NA 0.91822046 #> 3 A 0.7647987 0.20624914 15 0.4781883 0.1594836 NA 0.71636154 #> 4 A 0.7696877 0.81691683 20 0.7647987 0.4781883 0.1594836 0.20624914 #> 5 A 0.2685485 0.71585943 14 0.7696877 0.7647987 0.4781883 0.81691683 #> 6 B 0.6730459 0.06062449 14 0.2685485 0.7696877 0.7647987 0.71585943 #> 7 B 0.9787908 0.84710058 17 0.6730459 0.2685485 0.7696877 0.06062449 #> 8 B 0.8463270 0.84676044 8 0.9787908 0.6730459 0.2685485 0.84710058 #> 9 B 0.8566562 0.33261085 16 0.8463270 0.9787908 0.6730459 0.84676044 #> 10 B 0.4451601 0.55965050 16 0.8566562 0.8463270 0.9787908 0.33261085 #> 11 C 0.8382325 0.66946933 12 0.4451601 0.8566562 0.8463270 0.55965050 #> 12 C 0.5833169 0.25463848 18 0.8382325 0.4451601 0.8566562 0.66946933 #> 13 C 0.5109512 0.07917477 5 0.5833169 0.8382325 0.4451601 0.25463848 #> 14 C 0.2601681 0.15996809 6 0.5109512 0.5833169 0.8382325 0.07917477 #> 15 C 0.7494857 0.81639049 6 0.2601681 0.5109512 0.5833169 0.15996809 #> l2_y l3_y l1_z l2_z l3_z #> 1 NA NA NA NA NA #> 2 NA NA 9 NA NA #> 3 0.91822046 NA 19 9 NA #> 4 0.71636154 0.91822046 15 19 9 #> 5 0.20624914 0.71636154 20 15 19 #> 6 0.81691683 0.20624914 14 20 15 #> 7 0.71585943 0.81691683 14 14 20 #> 8 0.06062449 0.71585943 17 14 14 #> 9 0.84710058 0.06062449 8 17 14 #> 10 0.84676044 0.84710058 16 8 17 #> 11 0.33261085 0.84676044 16 16 8 #> 12 0.55965050 0.33261085 12 16 16 #> 13 0.66946933 0.55965050 18 12 16 #> 14 0.25463848 0.66946933 5 18 12 #> 15 0.07917477 0.25463848 6 5 18 log_at(Example, my_vars) #> category x y z ln_x ln_y ln_z #> 1 A 0.1594836 0.91822046 9 -1.83581396 -0.08531777 2.197225 #> 2 A 0.4781883 0.71636154 19 -0.73775063 -0.33357029 2.944439 #> 3 A 0.7647987 0.20624914 15 -0.26814262 -1.57867043 2.708050 #> 4 A 0.7696877 0.81691683 20 -0.26177046 -0.20221798 2.995732 #> 5 A 0.2685485 0.71585943 14 -1.31472376 -0.33427146 2.639057 #> 6 B 0.6730459 0.06062449 14 -0.39594173 -2.80305628 2.639057 #> 7 B 0.9787908 0.84710058 17 -0.02143736 -0.16593584 2.833213 #> 8 B 0.8463270 0.84676044 8 -0.16684950 -0.16633746 2.079442 #> 9 B 0.8566562 0.33261085 16 -0.15471866 -1.10078209 2.772589 #> 10 B 0.4451601 0.55965050 16 -0.80932117 -0.58044280 2.772589 #> 11 C 0.8382325 0.66946933 12 -0.17645973 -0.40126992 2.484907 #> 12 C 0.5833169 0.25463848 18 -0.53902464 -1.36791047 2.890372 #> 13 C 0.5109512 0.07917477 5 -0.67148128 -2.53609759 1.609438 #> 14 C 0.2601681 0.15996809 6 -1.34642717 -1.83278093 1.791759 #> 15 C 0.7494857 0.81639049 6 -0.28836799 -0.20286250 1.791759 log_at(Example, my_vars, plus_1 = TRUE) #> category x y z ln_x ln_y ln_z #> 1 A 0.1594836 0.91822046 9 0.1479748 0.65139791 2.302585 #> 2 A 0.4781883 0.71636154 19 0.3908172 0.54020667 2.995732 #> 3 A 0.7647987 0.20624914 15 0.5680366 0.18751566 2.772589 #> 4 A 0.7696877 0.81691683 20 0.5708031 0.59714102 3.044522 #> 5 A 0.2685485 0.71585943 14 0.2378733 0.53991408 2.708050 #> 6 B 0.6730459 0.06062449 14 0.5146459 0.05885788 2.708050 #> 7 B 0.9787908 0.84710058 17 0.6824859 0.61361716 2.890372 #> 8 B 0.8463270 0.84676044 8 0.6131982 0.61343299 2.197225 #> 9 B 0.8566562 0.33261085 16 0.6187771 0.28714006 2.833213 #> 10 B 0.4451601 0.55965050 16 0.3682201 0.44446176 2.833213 #> 11 C 0.8382325 0.66946933 12 0.6088045 0.51250581 2.564949 #> 12 C 0.5833169 0.25463848 18 0.4595220 0.22684747 2.944439 #> 13 C 0.5109512 0.07917477 5 0.4127394 0.07619665 1.791759 #> 14 C 0.2601681 0.15996809 6 0.2312452 0.14839249 1.945910 #> 15 C 0.7494857 0.81639049 6 0.5593219 0.59685128 1.945910 mean_at(Example, my_vars) #> category x y z mean_x mean_y mean_z #> 1 A 0.1594836 0.91822046 9 0.6121894 0.533333 13 #> 2 A 0.4781883 0.71636154 19 0.6121894 0.533333 13 #> 3 A 0.7647987 0.20624914 15 0.6121894 0.533333 13 #> 4 A 0.7696877 0.81691683 20 0.6121894 0.533333 13 #> 5 A 0.2685485 0.71585943 14 0.6121894 0.533333 13 #> 6 B 0.6730459 0.06062449 14 0.6121894 0.533333 13 #> 7 B 0.9787908 0.84710058 17 0.6121894 0.533333 13 #> 8 B 0.8463270 0.84676044 8 0.6121894 0.533333 13 #> 9 B 0.8566562 0.33261085 16 0.6121894 0.533333 13 #> 10 B 0.4451601 0.55965050 16 0.6121894 0.533333 13 #> 11 C 0.8382325 0.66946933 12 0.6121894 0.533333 13 #> 12 C 0.5833169 0.25463848 18 0.6121894 0.533333 13 #> 13 C 0.5109512 0.07917477 5 0.6121894 0.533333 13 #> 14 C 0.2601681 0.15996809 6 0.6121894 0.533333 13 #> 15 C 0.7494857 0.81639049 6 0.6121894 0.533333 13 r1sd_at(Example, my_vars) #> category x y z s_x s_y s_z #> 1 A 0.1594836 0.91822046 9 -1.8112227 1.22348833 -0.7954674 #> 2 A 0.4781883 0.71636154 19 -0.5361226 0.58181493 1.1932011 #> 3 A 0.7647987 0.20624914 15 0.6105718 -1.03974121 0.3977337 #> 4 A 0.7696877 0.81691683 20 0.6301320 0.90146222 1.3920679 #> 5 A 0.2685485 0.71585943 14 -1.3748670 0.58021879 0.1988668 #> 6 B 0.6730459 0.06062449 14 0.2434797 -1.50265592 0.1988668 #> 7 B 0.9787908 0.84710058 17 1.4667290 0.99741097 0.7954674 #> 8 B 0.8463270 0.84676044 8 0.9367569 0.99632970 -0.9943342 #> 9 B 0.8566562 0.33261085 16 0.9780827 -0.63805992 0.5966005 #> 10 B 0.4451601 0.55965050 16 -0.6682645 0.08365854 0.5966005 #> 11 C 0.8382325 0.66946933 12 0.9043720 0.43275298 -0.1988668 #> 12 C 0.5833169 0.25463848 18 -0.1155155 -0.88592015 0.9943342 #> 13 C 0.5109512 0.07917477 5 -0.4050424 -1.44368791 -1.5909348 #> 14 C 0.2601681 0.15996809 6 -1.4083958 -1.18686040 -1.3920679 #> 15 C 0.7494857 0.81639049 6 0.5493064 0.89978905 -1.3920679 r2sd_at(Example, my_vars) #> category x y z z_x z_y z_z #> 1 A 0.1594836 0.91822046 9 -0.90561135 0.61174417 -0.39773369 #> 2 A 0.4781883 0.71636154 19 -0.26806132 0.29090746 0.59660054 #> 3 A 0.7647987 0.20624914 15 0.30528590 -0.51987061 0.19886685 #> 4 A 0.7696877 0.81691683 20 0.31506602 0.45073111 0.69603396 #> 5 A 0.2685485 0.71585943 14 -0.68743350 0.29010940 0.09943342 #> 6 B 0.6730459 0.06062449 14 0.12173984 -0.75132796 0.09943342 #> 7 B 0.9787908 0.84710058 17 0.73336452 0.49870548 0.39773369 #> 8 B 0.8463270 0.84676044 8 0.46837843 0.49816485 -0.49716712 #> 9 B 0.8566562 0.33261085 16 0.48904135 -0.31902996 0.29830027 #> 10 B 0.4451601 0.55965050 16 -0.33413225 0.04182927 0.29830027 #> 11 C 0.8382325 0.66946933 12 0.45218599 0.21637649 -0.09943342 #> 12 C 0.5833169 0.25463848 18 -0.05775774 -0.44296007 0.49716712 #> 13 C 0.5109512 0.07917477 5 -0.20252121 -0.72184395 -0.79546739 #> 14 C 0.2601681 0.15996809 6 -0.70419791 -0.59343020 -0.69603396 #> 15 C 0.7494857 0.81639049 6 0.27465322 0.44989453 -0.69603396"},{"path":"http://svmiller.com/reference/binred_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"binred_plot() provides diagnostic fit generalized linear model \"binning\" fitted residual values model showing may fall outside 95% error bounds.","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"","code":"binred_plot(model, nbins, plot = TRUE)"},{"path":"http://svmiller.com/reference/binred_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"model fitted GLM model, assuming link \"logit\" nbins number \"bins\" calculation. Defaults rounded square root number observations model absence user-specified override . plot logical, defaults TRUE. TRUE, function plots binned residuals. FALSE, function returns data frame binned residuals.","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"bindred_plot() returns plot ggplot2 object, default. y-axis mean residuals particular bin. x-axis mean fitted values bin. Error bounds 95%. LOESS smoother overlaid solid blue line. plot = FALSE, function returns data frame binned residuals summary whether residuals error bounds.","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"number bins user wants arbitrary. Gelman Hill (2007) say , larger data sets (n >= 100), number bins rounded-square root number observations model. models number observations 10 100, number bins 10. models fewer 10 observations, number bins rounded-number observations (divided 2). default rounded square root number observations model. smart want .","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/binred_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a Binned-Residual Plot from a Fitted Generalized Linear Model — binred_plot","text":"","code":"M1 <- glm(vs ~ mpg + cyl + drat, data=mtcars, family=binomial(link=\"logit\")) binred_plot(M1) #> 2 of 6 bins are inside the error bounds. That is approximately 33.33%. An ideal rate is 95%. An acceptable rate is 80%. Any lower than that typically indicates a questionable model fit. Inspect the returned plot for more. #> `geom_smooth()` using method = 'loess' and formula = 'y ~ x' #> Warning: Chernobyl! trL>n 6 #> Warning: Chernobyl! trL>n 6 #> Warning: NaNs produced #> Warning: NaNs produced #> Warning: no non-missing arguments to max; returning -Inf"},{"path":"http://svmiller.com/reference/carrec.html","id":null,"dir":"Reference","previous_headings":"","what":"Recode a Variable — carrec","title":"Recode a Variable — carrec","text":"recodes numeric vector, character vector, factor according fairly simple recode specifications former Stata users appreciate. Yes, taken John Fox's recode() unction car package. going carrec() (.e. shorthand car::recode(), phonetically : \"car-wreck\") package, additional shorthand carr thing. goal minimize number function clashes multiple packages use workflow. example: car, dplyr, Hmisc recode() functions. rely car package just function, conflicts tidyverse functions vital workflow.","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recode a Variable — carrec","text":"","code":"carrec(var, recodes, as_fac, as_num = TRUE, levels) carr(...)"},{"path":"http://svmiller.com/reference/carrec.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recode a Variable — carrec","text":"var numeric vector, character vector, factor recodes character string recode specifications: see , former Stata users find stuff familiar as_fac return factor; default TRUE var factor, FALSE otherwise as_num TRUE (default) .factor FALSE, result coerced numeric values result numeric. want 99% applications regression analysis. levels optional argument specifying order levels returned factor; default use sort order level names. ... optional, make shortcut (carr()) work","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recode a Variable — carrec","text":"carrec() returns vector, recoded specifications user. carr() simple shortcut forcarrec().","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Recode a Variable — carrec","text":"Recode specifications appear character string, separated semicolons (see examples ), form input=output. input value satisfies one specification, first (left right) applies. specification satisfied, input value carried result. NA allowed input output.","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Recode a Variable — carrec","text":"Fox, J. Weisberg, S. (2019). R Companion Applied Regression, Third Edition, Sage.","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Recode a Variable — carrec","text":"John Fox","code":""},{"path":"http://svmiller.com/reference/carrec.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recode a Variable — carrec","text":"","code":"x <- seq(1,10) carrec(x,\"0=0;1:2=1;3:5=2;6:10=3\") #> [1] 1 1 2 2 2 3 3 3 3 3"},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":null,"dir":"Reference","previous_headings":"","what":"Charitable Contributions Panel Data — charitable_contributions","title":"Charitable Contributions Panel Data — charitable_contributions","text":"toy panel data set charitable contributions across 10 years 47 taxpayers. useful illustrating estimation panel models.","code":""},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Charitable Contributions Panel Data — charitable_contributions","text":"","code":"charitable_contributions"},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Charitable Contributions Panel Data — charitable_contributions","text":"data frame 470 observations following 8 variables. subject numeric identifier subject time numeric time identifier, simple integer 1 10 charity sum cash property contributions, excluding carry-overs previous years income adjusted gross income price 1 minus marginal income tax rate, defined income prior contributions age dummy variable equals 1 respondent 64, 0 otherwise ms dummy variable equals 1 respondent married, 0 otherwise deps number claimed dependents, integer","code":""},{"path":"http://svmiller.com/reference/charitable_contributions.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Charitable Contributions Panel Data — charitable_contributions","text":"Frees (2003) nominal source data, appear toy data sets use book. turn cites Banerjee Frees (1995), though citation may meant 1997 article Journal American Statistical Association. actual source data obtained Gujarati (2012). underlying source raw data supposedly 1979-1988 Statistics Income Panel Individual Tax Returns. Given opacity data, temporal limitations, data used illustration inference. charitable contributions variable income variables clearly log-transformed. Banerjee Price (1997) seem imply price variable well.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate Data from Correlation Matrix — cor2data","title":"Simulate Data from Correlation Matrix — cor2data","text":"function simulate data correlation matrix. useful illustrating theoretical properties regressions population parameters known set advance.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate Data from Correlation Matrix — cor2data","text":"","code":"cor2data(cor, n, seed)"},{"path":"http://svmiller.com/reference/cor2data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate Data from Correlation Matrix — cor2data","text":"cor correlation matrix (class matrix) n number observations simulate seed optional parameter set seed. Omitting generates new simulations every time.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate Data from Correlation Matrix — cor2data","text":"cor2data() returns data frame observations simulated standard normal distribution, pre-set correlations.","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate Data from Correlation Matrix — cor2data","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/cor2data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate Data from Correlation Matrix — cor2data","text":"","code":"vars <- c(\"control\", \"treat\", \"instr\", \"e\") Correlations <- matrix(cbind(1, 0.001, 0.001, 0.001, 0.001, 1, 0.85, -0.5, 0.001, 0.85, 1, 0.001, 0.001, -0.5, 0.001, 1),nrow=4) rownames(Correlations) <- colnames(Correlations) <- vars cor2data(Correlations, 1000, 8675309) #> control treat instr e #> 1 -0.9965823534 0.7208272032 0.287418622 -0.2321504442 #> 2 1.0654160534 0.9882846616 0.854663879 -0.2618288603 #> 3 0.5720665025 0.9042493136 -0.047579748 -1.3874597782 #> 4 0.1500831690 -0.6598135468 -1.084751166 0.1370684857 #> 5 -0.4418016435 -0.9010785615 -0.845310165 0.0715451179 #> 6 1.9858258232 0.0459908373 -0.173578835 -0.4935294263 #> 7 -0.4148232405 0.6828190627 0.943919855 0.3814435259 #> 8 -0.1861047636 0.3827595299 0.523484928 0.4700195250 #> 9 1.5749027754 0.5901018972 0.177827618 -0.8610024992 #> 10 0.0639170359 -0.3134096367 -0.397293735 -0.0918152377 #> 11 -0.6352569781 -0.0044295617 0.020168913 0.2501361307 #> 12 -0.2520425211 -0.1512905007 -0.589533425 -1.2448928940 #> 13 0.2376632053 0.0363285505 0.101976860 0.1277191406 #> 14 -0.8255891282 -2.1360607862 -1.702933848 0.9465364294 #> 15 -0.6516404496 0.7280512605 1.171750633 0.5155311475 #> 16 1.5969914054 0.5656807418 0.399226237 -0.7703476721 #> 17 -0.1421471777 0.9006403891 0.954204281 -0.4455227784 #> 18 -1.3220235033 0.2264778728 0.009064416 0.0065538113 #> 19 -0.2246474073 -2.5970891486 -2.693111816 1.0079821345 #> 20 -0.9394608113 -0.8518025099 0.150778171 1.3469793319 #> 21 0.0860048804 1.2516636414 1.385327986 -0.1259933303 #> 22 0.7225398120 -1.1077042306 -0.535931502 1.4957405636 #> 23 0.0729270191 -1.3144007994 -1.014642021 0.9981142810 #> 24 -0.9260292594 -0.7883291769 -0.361114780 1.0344957861 #> 25 1.0869857446 0.1321047285 -0.094669783 0.2438068996 #> 26 0.7445376777 -1.3458827971 -0.969975143 0.9370971713 #> 27 -0.4636759603 0.2044783119 0.418151000 0.0377759209 #> 28 -1.0655403666 -0.2639171355 -1.106705577 -1.1383977098 #> 29 -0.5287039864 1.1551101158 1.358809253 0.1315152717 #> 30 -0.2198145126 1.6884225118 1.376292202 -1.2509852977 #> 31 1.4954756842 -0.6883876150 0.141941256 1.1002280342 #> 32 0.3025106599 0.4001815241 0.337654874 0.1698237556 #> 33 0.5959807404 -0.4945399548 -0.073658970 1.0189431924 #> 34 -0.7104127738 -0.7060516974 -0.143037533 0.9064538362 #> 35 -0.0514446168 0.0376181078 -0.004882204 -0.2263715972 #> 36 -0.6982394936 -1.6994442420 -0.922406514 1.3535014727 #> 37 -0.5044070188 -0.1559585619 0.483247583 1.1880071951 #> 38 1.3183341418 0.2652751179 0.057576689 -0.1052884727 #> 39 1.4227405795 -1.0976779308 -1.251364754 -0.3184538038 #> 40 -2.0903851451 -0.9361926111 -0.640157926 0.5808296039 #> 41 0.9157140623 0.7301538554 0.389022933 -0.8004601103 #> 42 -2.5986449276 -1.2471576837 -1.605230246 -0.0884306389 #> 43 -1.0827933118 1.0084724666 -0.255951690 -2.0251737305 #> 44 0.7107506137 0.2133284164 0.967608811 1.2282214916 #> 45 -0.5455656264 0.4292847814 -0.115005861 -1.2679471874 #> 46 1.9146288126 1.4422300806 1.313794767 -1.0812488577 #> 47 -0.0677403499 0.0014178806 -1.310050717 -2.2700781341 #> 48 -1.9030759641 0.9880112464 0.965876945 -0.8509524730 #> 49 0.1457032985 0.4464379034 0.691172211 0.5672443530 #> 50 0.8362996636 0.4565637332 0.684055018 0.6873292995 #> 51 -0.0883647604 0.0810519541 0.800131749 1.0746550525 #> 52 0.9875926895 -0.1437086878 -0.439892046 -0.4734800696 #> 53 0.8726287585 -1.2015007831 -0.912452889 0.6801547630 #> 54 2.8694916820 -1.2024765360 -1.235091185 0.3560542533 #> 55 2.1171789017 -0.2789195895 0.103711855 -0.0173371770 #> 56 0.3871995746 1.0305680297 1.148117138 -0.0667032728 #> 57 -0.0726821124 -0.0138292124 -1.446099596 -2.0674425463 #> 58 1.2316246793 1.5915372558 1.084209644 -0.7922543166 #> 59 -0.8905042661 -1.5542798939 -0.598894451 1.6960736036 #> 60 0.0548748434 -1.1205033546 -0.204322826 2.0539886074 #> 61 0.4856123438 -0.0738361454 -0.447533352 -0.2342989632 #> 62 -1.6609907926 -0.9196301146 -1.504469271 -0.3646811626 #> 63 -0.7475356712 -1.0299982665 -1.050883913 0.1909882843 #> 64 -0.1874381301 1.1561341740 0.488461352 -1.1746531529 #> 65 -0.1280866986 -1.8264393518 -1.271893286 1.3563195366 #> 66 1.0161407701 0.2732879917 0.759082747 1.1700572167 #> 67 -0.0295302106 -0.3974142596 -0.388279281 -0.1360664977 #> 68 0.6139023858 0.0600806651 -0.832884040 -1.2413581354 #> 69 -1.8605476217 0.8611896280 0.208222326 -1.3874016015 #> 70 0.5176822404 -0.3542435118 -0.402401985 0.6143337740 #> 71 1.8119469018 0.3284199945 0.263171240 -0.0842571110 #> 72 -0.7192566870 -0.1676594684 -1.401654429 -2.0711198450 #> 73 -0.4689301875 0.2178897637 -0.070228936 0.0174193448 #> 74 -0.1271406030 -1.0523956093 -0.511933181 0.8729266243 #> 75 -1.1372336125 1.0122075493 0.749032578 -0.7273827565 #> 76 0.2649755539 1.0364260330 0.603182721 -0.9363068185 #> 77 0.2215458135 1.0018328125 1.532088895 0.8469385896 #> 78 -0.2418763513 -0.0026224299 0.317268293 0.6142776491 #> 79 0.1914502549 -0.2086116513 0.181531556 0.5119477399 #> 80 1.8800048735 -0.3239213317 -0.820185321 -1.0391501073 #> 81 0.0082917758 -0.0432110486 -0.306725231 -0.6065549685 #> 82 0.6667589254 -2.4044473421 -1.667889940 1.6771065500 #> 83 0.9175493351 0.7405620424 0.847903202 0.0814382458 #> 84 -0.6274929339 -1.0379196426 -0.937744507 0.5679629185 #> 85 -2.3842849277 -1.0758225239 -1.593280834 -0.5959578697 #> 86 -2.1428924005 -0.4764390312 -1.056403131 -0.5356201190 #> 87 0.2287578980 -0.2345622485 -0.422246655 -0.3665414560 #> 88 0.0536125533 0.0995781237 0.625111473 0.6583542295 #> 89 1.2267928812 0.5508514231 0.679968304 -0.0940431842 #> 90 0.4780816209 0.3275146299 -0.371817907 -1.5343363202 #> 91 0.3390401636 -1.2040982561 -2.397138525 -0.9272155705 #> 92 -0.0076861861 -0.5841703177 0.185066391 1.0579120807 #> 93 1.5291182883 0.8415548048 0.415501082 -0.8642873881 #> 94 0.4121599446 0.4340511225 0.092346062 -0.5901927536 #> 95 -0.8235687345 -0.8165891495 -0.567970387 -0.4023676516 #> 96 0.4954646491 -0.0002349125 0.387900028 1.0438416893 #> 97 0.1266491400 -0.6790643671 0.043058380 1.3790941535 #> 98 -0.5742761291 1.5984695389 0.704856808 -2.3542412644 #> 99 1.0010295847 0.2412304459 -0.384928717 -1.5592964356 #> 100 -1.1497760224 -1.2753716389 -1.142056448 0.2193758575 #> 101 0.1492778982 -0.1878344589 0.278414430 0.4367586205 #> 102 1.3997262697 0.4133508578 -0.051626586 -0.5362754147 #> 103 -1.0861982967 0.0700424135 -0.106325586 -0.2547302592 #> 104 -1.7474647786 -0.1227033807 -0.012243466 0.1094999363 #> 105 1.4272417329 -0.7406452097 -0.581810418 0.4674830928 #> 106 -0.9862928998 -0.1929405800 1.167368135 1.6394755966 #> 107 1.2863867167 -0.9951159830 -1.012189573 0.4951382816 #> 108 -0.4154368119 0.2572890197 -0.014111532 -1.0238290290 #> 109 0.2383302354 2.0102969808 2.239901452 -0.3614790130 #> 110 -0.7402096283 -1.2716446692 -1.510427813 -0.4514989512 #> 111 2.0466815674 -0.4928636163 -0.604210230 0.3055909873 #> 112 -2.0885948386 -0.3708611734 -0.373955669 -0.0174903503 #> 113 -0.5253580619 0.4811786721 0.948507514 0.4269440303 #> 114 -1.3154965328 -0.3952516303 -0.278402001 0.0192981349 #> 115 -0.0303048245 0.2226108103 0.034709658 -0.4323617290 #> 116 0.5124912363 -0.5625252783 -1.492788514 -1.2750845973 #> 117 1.4027150278 -1.8468250323 -2.699885004 -1.3446254759 #> 118 -0.6843599054 -1.0991092799 -1.641203736 0.1529525415 #> 119 1.8252950496 -1.4469577484 -0.496995199 1.9405547298 #> 120 -0.0510802981 3.4127416222 2.966804373 -1.7116465498 #> 121 -0.7758053871 -0.2263915757 0.022063956 0.6432251810 #> 122 -0.0005582601 0.3585390693 0.634236312 0.7141508302 #> 123 0.9439207858 -0.7058713840 -0.629653884 0.4224257279 #> 124 -0.3935517312 -1.4327522470 -1.563415818 -0.1711132676 #> 125 1.1514813248 -0.2884294070 0.857540507 1.0142849980 #> 126 -0.3912258906 2.2123300336 1.057275663 -2.6091010052 #> 127 -0.5547058433 1.4099466118 0.940140437 -1.0799053201 #> 128 0.3878424248 -0.9867081529 -2.302795191 -1.5966073234 #> 129 1.7572682273 -0.4069311421 -0.322776824 0.6044065326 #> 130 0.2621760480 2.1378381939 2.485834233 -0.1112496036 #> 131 1.5285383011 0.4537084767 1.280318580 1.4632770031 #> 132 1.0636595441 0.7214983716 1.792670070 1.5226734410 #> 133 0.2661299867 0.3060146691 -1.180210087 -2.5028086036 #> 134 0.3898872330 -1.0414902417 -1.504870734 0.5376234616 #> 135 0.2823965234 0.4257273140 1.026864538 0.7606334073 #> 136 -1.0757606536 -1.4961322802 -1.450717577 0.6128116223 #> 137 -0.4716045915 -1.3673918777 -1.395710451 0.4436689822 #> 138 -0.4424640222 1.6483200693 2.212215349 0.5527104635 #> 139 -1.0262684941 -0.6913329273 -1.090292090 -0.9713300623 #> 140 0.7781756063 -0.0582186721 0.322760231 0.5025193014 #> 141 -0.8269995163 -0.1629937838 -0.131652145 0.4391648907 #> 142 0.7248825578 0.3828298091 0.234452233 -0.6325338227 #> 143 0.4571013558 -0.5978666319 -1.335766528 -0.7418804781 #> 144 -0.0802096753 -0.5319872317 -0.731627000 0.3842356647 #> 145 -2.2309976562 0.3083798619 -0.388946280 -1.2305794313 #> 146 0.9363910393 0.5448630454 0.922837506 0.1709309695 #> 147 -1.6663139173 0.8273801456 0.234860508 -0.9234716121 #> 148 -0.3158055757 -0.4872280447 0.136331022 1.1068604597 #> 149 1.1365227782 -1.0124328991 -1.187312042 -0.4356236313 #> 150 -0.6525849499 -0.3927670669 0.115626843 0.8535295583 #> 151 -0.9528813410 0.7777425800 0.580039919 -0.5775641155 #> 152 0.1131788577 -0.5663035470 0.864162929 2.1379974913 #> 153 0.3533720065 -1.3588584424 -1.158939547 -0.0021350440 #> 154 -0.5102808937 -1.7623467969 -1.483675925 0.8149103795 #> 155 -1.1867309068 0.1023429468 0.558628161 0.1671132169 #> 156 0.3666060039 -0.5590729597 -1.215733242 -0.9240624420 #> 157 2.4966007633 0.9940866361 0.252677123 -1.1833951997 #> 158 -0.9915848454 0.5353980635 -0.490589215 -1.7897328622 #> 159 -1.8849580713 1.6450591620 0.674987814 -2.3225385919 #> 160 0.1687427678 0.5417204857 0.257332509 -0.7896209422 #> 161 0.2091340054 -0.2354308223 -0.798440022 -0.6502562592 #> 162 0.5175410155 -0.0267999731 0.620030078 1.0501397413 #> 163 0.0909253216 -1.1861863609 -0.923506257 0.5622934454 #> 164 -2.2255201755 -0.1455957333 -0.639005185 -0.8918414268 #> 165 1.2820031223 0.8039236999 0.913827074 -0.0737098914 #> 166 -0.4358379868 1.0734230168 1.273311743 -0.2100532587 #> 167 1.2532314902 -0.5291994279 0.534139063 2.1869417346 #> 168 1.0223172006 0.5687467504 0.903411187 0.6261027703 #> 169 -0.5756838860 0.5964307130 1.015942880 0.4251927752 #> 170 -1.0986413625 -0.5056961803 -0.661587863 -0.0538951952 #> 171 -1.3897765802 -0.0626112008 0.160780005 0.3167848300 #> 172 0.0746746899 -1.2073097185 -1.459447439 -0.3336329100 #> 173 1.2060502336 0.1292531669 -0.382794857 -0.9884968772 #> 174 1.7170201266 0.3877822582 0.857906381 -0.1455073027 #> 175 0.9365307877 1.7577995665 2.390797144 0.3068649257 #> 176 -2.0417007436 -0.0261505651 -0.282652293 0.1079130368 #> 177 -0.2249723988 -0.9723234811 -1.488516843 -0.3782198015 #> 178 -1.2326383202 0.8761481986 1.156920639 0.5704553731 #> 179 0.4374933990 -0.6448234862 0.104012080 1.6918202360 #> 180 -0.8353385222 -0.3594569680 -0.678154444 0.2805590170 #> 181 -2.4682653911 0.4529616387 1.447409424 1.6059427309 #> 182 -0.8782746883 -0.9263071546 -0.568454195 0.5820833909 #> 183 2.0642082674 0.3918494391 0.608336379 0.6974516233 #> 184 -0.3623272868 0.5337175388 -0.241122803 -0.7156792161 #> 185 1.1992259260 0.5712763986 -0.018610264 -0.8216855895 #> 186 1.1566529876 2.3059498265 2.204193445 -0.6905793552 #> 187 -0.9467408667 0.8379282653 1.074336481 0.3709542444 #> 188 0.4216108419 -0.0775368945 0.004224884 0.5130498202 #> 189 -0.0213208768 0.3631307413 -0.533567742 -1.1107489581 #> 190 0.3691331549 -0.4352823774 -0.859039979 -0.6691177792 #> 191 0.7333818988 0.2156083596 -0.805457083 -1.6371378522 #> 192 -0.8328036383 0.6845159841 -0.253614857 -1.2719939835 #> 193 1.0290869320 0.4934103112 -0.381879147 -1.4001220145 #> 194 -0.0182059685 1.6461686776 0.770662096 -1.7830646353 #> 195 0.4414848629 0.1905353362 0.345400964 0.2168995830 #> 196 -1.0306940473 0.9846023302 0.954199845 -0.6928303889 #> 197 -1.0337461899 0.1133996422 -0.496983518 -1.2554102559 #> 198 -1.0482805159 0.9973878149 -0.159234931 -2.3523600989 #> 199 -0.5617622806 -1.7970369104 -0.648694172 2.3977650898 #> 200 -0.4923549495 0.3514079443 -0.098047038 -0.7165782768 #> 201 1.3438289133 -0.9305745716 -1.003301724 -0.0390457188 #> 202 2.1347890594 0.3716704697 0.220537935 -0.7268333842 #> 203 0.5029592102 0.2448305583 0.229626686 -0.2575460846 #> 204 -0.9016388060 -1.5795719936 -0.717380246 1.9531262993 #> 205 -0.4263660697 0.6307585589 -0.046904405 -0.8417062628 #> 206 0.3115565821 -1.2866067342 -1.023282417 0.8695940757 #> 207 0.2988763148 -0.9745552518 -1.383254275 -0.5947413117 #> 208 1.2361565709 1.4150352851 1.031871616 -0.8904936580 #> 209 0.3725077679 0.7942484751 0.132774271 -0.7838227572 #> 210 -1.1750773417 0.5637584562 0.851171556 0.3130932654 #> 211 0.4965627441 0.2859975820 -0.293047763 -0.5899249310 #> 212 0.9325149534 1.4457924185 1.743031573 0.5754142256 #> 213 -0.7193845744 -1.1378050408 -0.577649370 0.9627493326 #> 214 -0.6920256098 -1.4194196331 -1.203043268 1.2643590826 #> 215 -2.0708013532 -0.6500758304 -0.023345928 1.2083537898 #> 216 -1.1885767601 -0.5017115532 -0.271143675 -0.5812068578 #> 217 1.0196957278 -0.8610112878 -0.551093910 0.1291345517 #> 218 -0.9801600387 -0.2082992619 0.389801964 0.8730471608 #> 219 -0.3048293564 0.7502682700 1.029307885 0.5022445041 #> 220 0.3891123079 -1.4515286003 -0.341255446 2.0239422442 #> 221 0.0480109920 0.1224140864 0.892263451 1.4284697193 #> 222 -0.7807094420 0.8781411585 0.973867116 -0.5928480434 #> 223 0.3739462760 -0.8071012499 -0.693135292 0.3111877553 #> 224 -0.5206896437 -1.7094542626 -0.948415367 2.3886446295 #> 225 1.3589583509 -0.9365137448 -0.368246157 1.5254623275 #> 226 -0.1902957880 0.5583270618 0.944778218 0.8145860387 #> 227 0.8426863822 1.2789201420 0.648705397 -1.4287494782 #> 228 0.7486460396 0.3554323220 -0.028549604 -0.8517924820 #> 229 0.3735456722 1.4573323841 1.318270463 -0.3206674503 #> 230 -0.7044303714 -0.4210005692 -0.122780414 0.6560503234 #> 231 -0.3151017812 -2.5245049596 -3.216464771 -0.2625191561 #> 232 -0.1678472734 -0.1113213979 -0.690901705 -1.1625406938 #> 233 -0.4443966995 0.6058941835 -0.166939722 -1.1096497136 #> 234 -1.4453786361 -0.7761605000 -1.031998488 -0.9941266056 #> 235 0.0107456987 0.2661966717 -0.391321737 -1.0043260461 #> 236 2.3317732785 -0.0823524628 0.021934832 0.2577879525 #> 237 0.1533153891 1.8133084612 1.852670561 -0.6178718603 #> 238 -1.7078257765 0.5809316555 1.379390135 0.2923790416 #> 239 0.0255381778 0.0126695856 -0.641503092 -1.1074941365 #> 240 -0.8791096861 0.6689311324 -0.388580194 -1.9334251873 #> 241 0.9540100928 -0.2360637047 -0.993013280 -1.3823952457 #> 242 -0.0180071628 -0.4643724382 -0.619287402 -0.2720526855 #> 243 -0.0712532550 -0.1695863082 -0.040577592 0.6303829396 #> 244 0.4990261682 0.1983377790 0.985607987 1.2279198734 #> 245 -0.3182756389 -0.9861697977 -0.871669247 0.5164891305 #> 246 0.3689123669 2.2379268380 2.038814831 -1.0210297753 #> 247 -0.7540141567 0.0360802881 0.876243245 1.1994393013 #> 248 -1.7738158349 -0.3962555216 -0.931874283 -1.2129961044 #> 249 0.9244368621 -1.3796205570 -0.629627354 1.7208066084 #> 250 1.1331602020 0.2442510501 -0.336933015 -1.1373761986 #> 251 -0.2674132575 -1.2634592822 -1.690355198 -0.0975583499 #> 252 1.6318222029 -0.2327109961 0.144170269 0.9408143231 #> 253 0.1051333094 0.1612631385 0.464902466 0.3053051776 #> 254 -1.3313872948 0.4060363766 -0.073564495 -0.7883624695 #> 255 -0.1029043378 0.5271073142 0.464145277 -0.0168736887 #> 256 -1.0501151829 1.4205127409 1.587905671 0.3326375027 #> 257 1.5842152883 -0.9701860555 -1.143126556 0.6259898261 #> 258 1.2641116408 -0.5373803362 -0.306766416 0.6633805100 #> 259 -1.1991583710 0.9388782635 1.165766248 0.1458027912 #> 260 -0.5839066312 -1.6538989171 -1.011080747 1.1684412153 #> 261 1.5844734828 0.4286997540 -0.238708420 -0.8713216856 #> 262 -2.6945196593 -0.4815923633 -0.535670337 -0.0017371586 #> 263 0.5454795327 0.0418462623 -0.549961196 -0.5534608490 #> 264 -0.9506309332 0.1965712386 0.443794509 0.1765411871 #> 265 0.7514034416 0.2406764140 0.579317408 0.5815578804 #> 266 0.6142840582 0.8469276132 0.394999941 -0.6755548287 #> 267 -0.4919001423 -1.1048747964 -0.679254326 0.3342529144 #> 268 1.0143629916 -0.6647525413 -1.182006730 -0.7184851595 #> 269 -0.3153580379 -0.2381459381 -0.705346260 -0.6934713197 #> 270 0.6325127572 0.6132209060 0.030091989 -1.0159158140 #> 271 0.8749485655 1.7581064136 1.456690652 -0.7531915512 #> 272 -0.8460066431 0.8281121824 1.068446301 -0.3335038297 #> 273 0.5448628782 0.9754610260 0.658936208 -1.2118555876 #> 274 0.4665643847 -0.8202513758 0.175764319 1.5790956599 #> 275 -1.5169966854 -0.8295641193 -0.313422978 0.6962046037 #> 276 0.9876591908 -0.8968688473 -1.777296315 -1.1713239772 #> 277 0.1293742393 1.1218232146 1.565713286 0.6381700248 #> 278 0.3752447946 0.4804923916 -0.274679522 -0.9758553360 #> 279 -0.0238203074 0.1374159843 0.378496456 0.4855705315 #> 280 -1.6291997286 0.2742964141 0.244684310 -0.7070943938 #> 281 -1.6699517714 1.1292589485 0.933862913 -0.6966269628 #> 282 0.3636184086 0.7519561838 1.212753893 0.1175917910 #> 283 2.1158338281 -0.1591687702 0.573899370 0.8984556679 #> 284 0.4324093988 -1.8064270319 -0.991296215 2.2448631295 #> 285 -0.3625831851 -0.7226585150 -0.745151834 0.5812348801 #> 286 -0.6285519008 0.5434036571 0.451718361 -0.2651429282 #> 287 0.9612526587 1.0020570444 1.307535982 -0.1084146433 #> 288 0.7018444191 0.5197885720 0.374029934 -0.1622017582 #> 289 -0.1901001582 -0.5561041378 -0.806349033 0.5274962196 #> 290 0.1829207990 -0.3390565172 -0.181038984 0.2496733341 #> 291 0.9286072852 0.3951947465 0.147856177 -0.8698139627 #> 292 0.2494420210 0.2550107506 -0.188711569 -0.4452841919 #> 293 -0.2398222790 0.5349858279 0.707468393 0.7915199311 #> 294 -1.6477069868 0.6508774297 0.615926241 -0.3869756141 #> 295 -1.1604467232 -0.0360506117 1.220157923 2.3192568643 #> 296 1.6752822367 0.2648159369 -0.056379112 -0.5357413910 #> 297 1.3916675385 0.5873993404 0.048830227 -1.8262191834 #> 298 -0.1026699377 -2.5441943458 -1.123517310 2.6627810984 #> 299 -0.9011659158 -0.5907666882 -0.712635239 -0.4363938110 #> 300 0.1009469272 -0.2512645456 -0.428644169 -0.2196907328 #> 301 0.5701397198 0.6229924238 -0.411861891 -1.4752699656 #> 302 -0.4913474370 2.5589252171 2.109169293 -1.5118205257 #> 303 -0.2740915671 1.6067836304 1.320835189 -0.8019768852 #> 304 -0.1493990078 -1.1940669112 -0.601723162 1.0104986959 #> 305 0.1806294429 0.3618992058 -0.426356061 -1.2284893588 #> 306 0.9203113540 0.8471292841 0.976520145 -0.0505772592 #> 307 0.6363767277 -1.1198105112 -1.651323126 -0.5850667997 #> 308 -0.0717002434 -1.4458888461 -1.432950866 0.9059859784 #> 309 0.1417651321 -0.9765072644 -1.717407807 -0.3215750427 #> 310 0.0765428275 0.3714029420 0.454733385 -0.3511896609 #> 311 -0.5125867486 0.9225335720 1.071650268 -0.0639684412 #> 312 0.4592486300 -1.9371112036 -0.947501824 2.9330613046 #> 313 0.7145027507 -0.0743239280 -0.277175214 -0.4048250364 #> 314 -1.5428035777 -0.2163269309 -0.251079553 -0.2636734044 #> 315 0.2061877846 0.6356931922 0.261243813 -0.7846487128 #> 316 -1.1616292189 -0.0515683838 0.029608557 -0.1323032707 #> 317 -1.7157991732 0.6936224222 0.131115662 -1.3314650774 #> 318 1.0519574733 1.9078787312 2.073076887 -0.1455859829 #> 319 1.0116796542 -0.6329067385 -0.671120203 0.1189316110 #> 320 0.1847126789 0.4380002345 -0.875706407 -1.6711166980 #> 321 1.6767425787 0.5010694130 -0.456372462 -2.0617870776 #> 322 -0.4534593889 1.5767933183 1.083570461 -1.6729442999 #> 323 0.7883146965 1.4165491436 0.809588321 -1.5257566939 #> 324 -1.0750273253 0.2460655546 0.101374746 -0.1026141631 #> 325 -1.4312032525 1.7954515293 1.594907950 -0.4779589391 #> 326 -1.4320176152 0.6985054259 -0.035443174 -1.5690386149 #> 327 0.7132062860 -0.4226373411 -0.640128070 -0.1751268305 #> 328 -0.4070435818 2.0118748925 1.187285069 -1.7067452023 #> 329 0.8955826542 0.6570106790 0.616039350 -0.2185563354 #> 330 1.0609209825 0.4140871392 -0.151646120 -0.8269233665 #> 331 -0.1283071285 -0.2056209458 -0.527976391 -0.2152962214 #> 332 -0.3534121427 -0.1714811880 -0.595512425 -0.6705048718 #> 333 0.3905649031 1.9834950801 2.331166768 -0.1366979754 #> 334 -1.4406009960 0.4863349765 0.359713075 -0.0636714628 #> 335 0.2101222473 -0.0369538087 0.003392888 0.3434410124 #> 336 -0.1960052650 0.8302718114 1.305331937 0.3763809018 #> 337 1.6459129661 2.4942973841 2.961324135 0.0324389514 #> 338 0.7417086124 -0.5328064896 0.440451601 1.5282767958 #> 339 1.4171752132 0.3031856504 1.159765491 1.7817615015 #> 340 0.1618826881 -1.5363450575 -0.667576964 1.3800910026 #> 341 1.0530199137 -0.7880203428 -0.354450239 1.2466205734 #> 342 -0.8629300826 -0.6170369925 -0.567648774 0.4307236522 #> 343 -0.9876900868 0.2142686499 -0.486533035 -1.2589039968 #> 344 -0.1765111396 0.7279805859 -0.049142366 -1.5647595850 #> 345 1.4989045992 -1.8134003480 -1.709435893 0.1217705949 #> 346 -0.3872961187 -1.1599387007 -1.036779106 0.6755510838 #> 347 -0.2844547099 0.3509265127 -0.520787961 -1.2609036056 #> 348 0.7319427364 -0.5004616837 0.284351916 1.7849449723 #> 349 -0.1916819763 -0.4810719573 -0.733740352 -0.0415153921 #> 350 0.8569219797 0.0347175230 0.122578553 0.3911469185 #> 351 -0.6528180628 0.6176099289 1.522255162 1.4603993223 #> 352 0.8444521334 1.5665165850 1.768368897 0.3728710132 #> 353 0.1937976228 -0.3287726153 -0.500388400 0.2252237539 #> 354 3.5047769131 -0.8626272391 -0.048541472 1.6422252584 #> 355 1.2304051311 -0.7528533563 -0.615962290 0.2766748568 #> 356 1.2874595865 0.4893891858 0.113566248 -0.1678104008 #> 357 -1.6676795461 -0.7063969362 -0.091373772 1.2031327289 #> 358 -0.0730305844 -1.5461599430 -1.814237778 0.5295763521 #> 359 0.3660375713 -0.7516096867 0.818604647 2.3462634762 #> 360 -0.4034299422 -1.4190953257 -1.661267452 0.0624119982 #> 361 -0.6209244841 0.3658087704 0.659931242 0.3250339496 #> 362 -0.4087582719 -0.3829469582 0.173965738 1.0231314989 #> 363 -0.6853750355 -0.4838779967 -1.037893590 -0.5621468821 #> 364 -1.1746949951 -0.6560346275 -0.765654875 -0.2937199527 #> 365 0.0243395012 -0.1221776746 -0.318842198 -0.6656854212 #> 366 1.7476997203 0.8977599137 0.531026617 -0.3695171302 #> 367 -0.9797732090 0.2838708503 -0.665431677 -2.0005838538 #> 368 -0.1552940467 -0.2155429835 -0.209894612 -0.1556285159 #> 369 0.7056420115 0.3581111943 0.794260947 0.5860510366 #> 370 -0.8719316004 1.5856655382 2.001702058 0.1163616723 #> 371 1.8819858033 0.0694265719 -0.984684377 -1.2980457299 #> 372 2.7639737951 0.5580330448 1.263781206 0.9627958471 #> 373 0.1525976701 0.3864863275 0.023111239 -0.7168214841 #> 374 0.4356820263 0.9880301825 1.099954318 -0.2082770965 #> 375 -0.6491587611 -1.3918886630 -1.713625527 -0.4386774295 #> 376 0.6821681711 0.7439039785 1.031912434 -0.1573269000 #> 377 -1.0079860200 -1.9077195878 -1.131081010 2.3369610917 #> 378 -0.5158933810 -1.1859040237 -2.021058030 -0.8421650458 #> 379 0.1399412287 0.1941451493 -0.675330140 -1.6568894745 #> 380 0.5022471741 0.0512675518 0.497222646 0.3764745375 #> 381 0.3455305280 0.2720032247 1.096770120 1.7558703391 #> 382 -1.2599196267 1.9275649222 0.790693848 -1.8365292004 #> 383 2.0546519667 -0.8649065014 -0.019558686 1.5142767611 #> 384 -1.3087259840 0.8783222501 0.680309390 -0.4148925084 #> 385 1.2606879261 1.8677012482 1.035748203 -1.7954234500 #> 386 -0.7347787143 -0.4010886457 -0.803652529 -0.7055352059 #> 387 -0.8432939071 -0.6918389552 -0.341437331 0.5208920877 #> 388 0.3567822204 0.5441837150 0.214405678 -0.8707356167 #> 389 0.2995354832 -0.6686016983 -0.776746060 -0.1017740490 #> 390 -0.9658036496 0.0780165771 0.601682103 1.0329565320 #> 391 0.1072444616 0.0337390463 0.559038450 0.9212212674 #> 392 -0.8176100869 -1.7965263695 -1.549527885 0.5502598651 #> 393 -0.3508041173 1.6793515928 1.201625854 -1.0342441839 #> 394 0.7157549184 0.9789819107 0.287374468 -1.6216453265 #> 395 -1.7987508315 1.1070478197 1.020053987 -0.1828728483 #> 396 0.6221782252 -0.6611676555 -0.331243850 0.4988969582 #> 397 0.1174344264 3.3352403176 2.666975376 -1.6115548384 #> 398 1.5395038369 -2.3561705634 -1.139170219 1.7928743752 #> 399 -2.2636579988 0.0323317977 -0.473795529 -0.4402924935 #> 400 0.5412221902 -0.1134194763 0.020237679 0.8065687922 #> 401 -0.3883670265 0.2066031424 -0.013513493 -0.6038911526 #> 402 1.1805162024 -1.9626380877 -1.769164440 0.7463525440 #> 403 -1.2090300199 -1.0515255127 -1.880900508 -0.8593334869 #> 404 0.0751682157 -0.4169381547 -0.858112025 -0.6402859630 #> 405 -0.7306621351 1.0826530380 1.616268694 0.4389279579 #> 406 1.2617190562 -1.4385945367 -0.554434874 1.5789736000 #> 407 -0.9157153728 0.6563302076 0.529551129 0.1258161329 #> 408 -1.0938949178 -1.4318848990 -1.386201461 0.7953393148 #> 409 -0.2091014900 -0.2585955465 -0.837508710 -0.5112342547 #> 410 0.6991478407 -1.9312236820 -2.071420127 0.7139383502 #> 411 1.8323377590 -0.3377152571 -0.511234582 -0.5473310492 #> 412 1.1989168116 1.7912426612 2.427893021 -0.1472047951 #> 413 0.1132024951 0.2783492412 0.127021569 -0.1659268428 #> 414 -1.6113347125 0.5693820410 -0.208477322 -1.5175355325 #> 415 0.3939993845 -0.1246163708 -0.390615445 -0.5688216603 #> 416 -0.1497863962 2.4769721725 2.042417579 -1.1125542657 #> 417 -1.8690536497 -1.9470285568 -1.937182029 0.6132399410 #> 418 -1.0673105099 -0.6330693498 -0.164061919 0.8946473126 #> 419 -0.0210945084 -0.6511790266 0.110684473 1.0962611272 #> 420 -0.1371906850 -0.6563974548 -1.091420044 -0.6866680373 #> 421 1.6004581583 1.6354727668 1.481626336 -0.5908770201 #> 422 -0.2406808938 0.1501999293 0.027759089 -0.0840291248 #> 423 -0.1673739516 0.9753319019 1.460596125 0.1860894328 #> 424 -1.9605639591 -1.4554562550 -1.963552655 -0.5324245422 #> 425 -1.8590150046 0.1295363724 0.619646214 0.4816644727 #> 426 -0.4903491998 -1.3143105886 -0.415370462 2.1002882642 #> 427 1.2959768232 0.8198351128 0.959415808 -0.8609090413 #> 428 0.3227093984 -1.1299253801 -1.487175634 0.2031890243 #> 429 -0.5781547884 1.6876257599 2.174394740 0.1301641235 #> 430 1.3035753661 0.6790823388 1.969242879 1.5984857543 #> 431 1.1240110132 -0.6406744664 -0.783901680 -0.3486267810 #> 432 -0.5612869711 0.7083478457 1.094187526 -0.0173927132 #> 433 1.1457205744 -0.0179219044 -0.367075450 -0.3710182244 #> 434 -0.0832116148 1.1683055303 1.748909023 0.4106167657 #> 435 0.4639267373 0.0176269782 -0.323739283 -0.0249793922 #> 436 0.3153427643 0.1968412538 0.297764793 -0.3039828524 #> 437 1.3323659610 1.1833202295 0.117299682 -1.8719901694 #> 438 -0.3193678977 -0.8370973446 -0.377563344 0.9184755016 #> 439 -0.6204523338 -0.0718608214 -0.049270240 -0.2355571408 #> 440 1.7140663681 -0.1027218539 -0.364470275 -0.6822101098 #> 441 -0.6592895406 -3.8238870922 -3.445265650 1.3482752127 #> 442 1.9091456991 0.1094721277 0.107554720 0.2468237048 #> 443 0.0755031232 1.7269960070 2.030227355 0.0305703663 #> 444 -0.5390916016 0.9069451004 1.392550606 0.3156307953 #> 445 -0.0763500983 0.5008019028 0.740670340 -0.1193795189 #> 446 -0.0947008661 -0.7775112333 -0.378722644 0.6294095672 #> 447 -0.7092386416 2.3810561905 2.610861357 -0.8251043546 #> 448 0.0274547172 -0.6351171188 -0.559090018 0.4447649613 #> 449 -3.4938710319 0.4791489283 0.277657381 -0.7860268320 #> 450 0.8844889488 2.8696593495 2.213195425 -1.6852967546 #> 451 1.2534049120 -1.4836162482 -1.312330705 0.6662054083 #> 452 0.6691205893 -1.5159561341 -1.176182025 0.5750321208 #> 453 1.5184719107 0.1221472327 0.481478338 0.5287590025 #> 454 -1.0373180160 0.6236212271 1.535989664 0.5620585874 #> 455 -0.7714622880 -0.2072375811 0.348001712 0.4293597846 #> 456 -0.1169579173 -0.5251265707 -0.352018275 0.0789271415 #> 457 -0.5614108264 1.3869330708 0.296752396 -1.8669780140 #> 458 -1.4658804728 2.9201744873 2.538855205 -1.6840984555 #> 459 -1.1359181676 -0.7193724932 -0.360821125 0.6975076820 #> 460 -0.4276828785 -0.8993423663 -0.254382448 1.0392666944 #> 461 -0.7735442977 0.2024807164 -0.603161818 -1.4166034403 #> 462 0.4163032319 0.3597513314 0.773153500 0.8779510637 #> 463 1.5326106370 1.8134772650 0.893983360 -2.1019119205 #> 464 -0.7167785697 0.5306633894 1.033484965 0.9572808828 #> 465 -0.8154305649 -0.5904980754 -0.858174446 0.0202221193 #> 466 -1.0144247681 0.9329584132 0.404129107 -0.5607129809 #> 467 -0.7318181844 -1.1962825605 -0.100223826 2.5070383757 #> 468 -1.8453004250 -0.9009120323 -0.403344746 1.2176879731 #> 469 1.3870885854 -0.7012518980 -0.960796822 -0.1518644253 #> 470 -1.7467760961 -0.2343899389 -0.021119812 0.2596499921 #> 471 -1.2970987911 -1.2152250863 -1.589095516 -0.8417944691 #> 472 -0.6623913430 -1.2052222597 -0.767787239 0.9356664583 #> 473 1.0996540984 -0.5710242949 -0.367498394 0.3670024870 #> 474 -1.0948033239 -0.7663466524 -0.865196910 0.6727049909 #> 475 0.3566826989 -1.7709173698 -2.113397069 0.1006528806 #> 476 0.6492101058 0.8592629997 0.720058663 0.1889318799 #> 477 0.9689532264 0.7495438495 0.635040039 -0.8923396041 #> 478 1.7007627843 0.3314883613 0.655509542 0.9933737873 #> 479 1.1993005619 1.3029769033 0.693490794 -1.3237515340 #> 480 -0.1500743042 0.6328349666 0.499738198 -0.6900237450 #> 481 0.5979976053 -1.0153624730 -1.061801848 0.0168207668 #> 482 -0.9928389088 1.4986734233 2.222078795 0.2732860747 #> 483 0.6354194133 0.0449804106 0.450477444 0.4548369987 #> 484 0.5975407876 -0.3824700559 0.312395743 1.3283006949 #> 485 0.2404603292 -0.6435384591 0.010159232 1.3547963990 #> 486 -0.9290082873 0.6115281446 0.979411524 0.8089188105 #> 487 -0.5105971387 -0.0727964314 -0.997812258 -1.2806916032 #> 488 -0.2311911458 -0.4545535930 -0.019298367 0.9492647282 #> 489 -0.7237933526 -0.3616559314 -1.059738831 -0.9812868488 #> 490 -2.4105663041 1.4126962127 1.973259654 0.0035498854 #> 491 -1.1583324765 -0.7107568467 -0.406755691 0.8037657044 #> 492 1.9852747606 0.0828622798 0.609941850 0.5856199895 #> 493 -0.8408902318 -0.1243277115 -0.328863381 -0.2184652939 #> 494 0.1579296250 -0.2329566633 -0.014279813 0.6690834390 #> 495 -0.0613177540 0.9456869080 0.987397606 -0.3159404009 #> 496 -0.7841736993 0.5377045450 0.433629649 -0.3861994868 #> 497 -0.9096832833 -0.3474134275 0.106037915 0.7553339890 #> 498 0.2468158566 -0.9061444538 -1.168358492 -0.3849403759 #> 499 0.1750499771 0.7238555700 0.009476823 -1.5959764452 #> 500 -0.2841790773 -1.8918450577 -1.740480605 1.0731143517 #> 501 1.3044256709 1.4741584346 1.887350836 -0.3694761355 #> 502 0.7948228638 -1.0285468613 -1.017446315 0.4212327561 #> 503 1.1886186729 -2.0256381001 -1.787939649 0.6246191072 #> 504 -0.4912705952 -1.0827762837 0.180941733 1.3508941412 #> 505 -0.8124736469 -0.8530918356 -0.178065382 0.5778358567 #> 506 0.3693651313 0.0476089453 0.132283708 0.0872234498 #> 507 -0.9215013884 -1.8451862908 -0.514346598 2.1009528733 #> 508 0.5096286003 -0.0975276551 0.316117671 0.6955295280 #> 509 1.2594178723 1.9401694143 1.722740945 -1.4014851259 #> 510 -2.1010216479 -0.3432872937 -0.760484485 -0.1893832663 #> 511 1.5395901955 -0.4750771419 -1.085109748 -0.3909975626 #> 512 0.2183582808 0.9583743730 0.041738039 -2.3306842516 #> 513 1.7552000237 -0.1925857383 -0.506577415 -0.4068469877 #> 514 -0.7753387302 0.0596751475 -1.083517682 -1.5427498280 #> 515 -1.6245955956 1.4151686262 1.937086769 0.2015833558 #> 516 -1.0025285823 -2.0293041900 -2.945392134 -1.1527377591 #> 517 -2.1453336875 0.6574007512 0.434307088 0.1266418181 #> 518 -0.0922502622 -1.1643416151 -1.145531760 0.2277435434 #> 519 0.0025403809 1.5096024166 2.083169312 0.6040794429 #> 520 1.6110085847 0.4540982199 1.474742208 1.6646537598 #> 521 0.9968168118 0.3893105520 0.567220345 0.0981453711 #> 522 0.2627224346 -0.6511599231 0.233953444 1.3436786042 #> 523 -0.6995011987 0.3678536370 0.358466596 0.0807081165 #> 524 1.8441719823 -1.4410218386 -0.531263727 1.5183015015 #> 525 1.3297458484 -1.0979522025 -0.469124381 1.2610213259 #> 526 0.6789518102 0.7481040128 0.886099625 0.4325220184 #> 527 -1.2274096192 0.4772752028 1.310976368 0.9034869503 #> 528 -0.3734748332 1.9396931125 2.360986577 0.0495707540 #> 529 -0.5911746899 0.6832511272 1.196287406 0.9863760671 #> 530 0.6107274366 -0.2444192174 -0.377007071 0.1561519039 #> 531 -0.3724498642 0.4459706057 1.049556137 1.0911240751 #> 532 0.1566416633 -1.6502878054 -1.536898828 0.5441093646 #> 533 -0.3364111759 -1.4237459239 -1.021588234 1.2255158216 #> 534 0.3260204547 1.0178046475 1.502469702 1.0754718171 #> 535 1.1598297661 0.4756513689 0.920276708 0.8247597402 #> 536 0.6554945729 -0.3729172644 -0.463539207 -0.1547347582 #> 537 1.0429722566 0.5797611106 1.137885637 0.7676123393 #> 538 -0.3007149967 -0.8743458812 -0.769593369 0.4327040299 #> 539 -0.6470825287 -1.8160434383 -1.594156463 0.4766732382 #> 540 -1.3042977710 -0.4696564375 -0.053524779 0.9636035711 #> 541 -0.5040035511 1.3504208065 2.009321096 0.9079172148 #> 542 -0.6854980593 -1.9831829626 -1.372403740 1.3913191676 #> 543 0.6400849904 -0.1105197075 0.146678756 0.3654752357 #> 544 1.1545063746 1.9001920065 1.088932545 -1.4844124026 #> 545 -0.5536701674 -0.0451630009 -0.388878423 -0.1491018689 #> 546 0.2448451932 -1.0648436827 -1.195746084 0.0153615060 #> 547 -0.4913264427 -1.0420877053 -0.227543846 1.5903748172 #> 548 0.9089644049 -0.1770320379 -0.710944597 -0.8591128912 #> 549 -1.4480886049 0.4730045494 1.718534884 1.2888682151 #> 550 1.1290782321 0.1367610712 0.464622596 0.2568278032 #> 551 1.1592133181 0.2630603038 0.743203022 0.8852096757 #> 552 -0.4417653023 0.2765783374 -0.598622595 -1.5280363965 #> 553 0.8421254078 -0.6173575282 -0.240395892 0.9044368833 #> 554 0.2193056555 1.5048842364 1.253244569 -0.9775184146 #> 555 0.2230189795 -1.0376108478 -0.805230549 0.7326335803 #> 556 0.2218885393 1.1586871948 0.207964974 -1.4397610958 #> 557 0.4118867959 1.6318253055 0.980655721 -1.2914290881 #> 558 -1.4991407534 -0.7200037362 0.381143042 1.4909826860 #> 559 0.3373152269 -0.9130749806 -1.369939542 0.0496168734 #> 560 1.6264893201 -0.2471882375 0.118611293 0.6202511068 #> 561 -0.7127661082 0.5691360371 0.208407489 -1.2477273909 #> 562 -0.7147363088 -1.3377610171 -0.771861723 1.0100582351 #> 563 -0.1864531023 0.3745124236 0.081363392 -0.5113616930 #> 564 1.2703105629 0.6329692899 0.937485544 0.2066546330 #> 565 0.6715115868 -1.4026014371 -0.530886203 1.8757254748 #> 566 -0.2935353680 1.4030908311 0.827795196 -1.1142051299 #> 567 0.7157704192 -0.1667592920 0.202055324 0.6641135071 #> 568 -0.0777541743 -0.5284464406 -0.667341663 -0.2572392735 #> 569 -0.9287632275 0.8260561942 0.070102632 -1.2430548917 #> 570 -0.3854772316 -0.0754859197 -0.324002801 -0.3335127044 #> 571 1.1006506323 0.6405915387 0.999909004 0.2944852796 #> 572 0.2680982866 -1.8411781186 -2.013433327 -0.1432620442 #> 573 -0.6783623211 -0.5343196411 -0.332660869 -0.0985449689 #> 574 1.2509244301 -2.1688132978 -1.510598843 1.8536389799 #> 575 -1.2131812235 1.2807124620 0.725632733 -0.8930670837 #> 576 1.0160959012 0.4281976364 0.339545456 -0.6305178197 #> 577 -0.5054480299 -1.4065961300 -1.369956588 0.0643886395 #> 578 -0.6950899022 0.5589463602 0.687815848 -0.0813452606 #> 579 -0.8740647739 -0.6874977354 -0.722830460 -0.1358448282 #> 580 0.5647821482 0.1284506568 -0.779091747 -0.8775250073 #> 581 -0.5599329697 1.1509443637 0.865275448 -0.3248129126 #> 582 1.0685176674 0.8042340665 0.772010795 -0.7248837972 #> 583 1.0578666390 0.9839777411 0.797985013 -0.5382368418 #> 584 -1.3180109144 0.6000965847 0.932220883 0.1983195302 #> 585 -2.0589108575 -0.3678046329 -1.074369988 -0.7923278064 #> 586 0.4032351738 0.1527296615 -0.312658916 -0.6284244236 #> 587 -2.1233863948 -0.1244076262 0.002743405 -0.1860135623 #> 588 0.5578353624 -0.9737917906 -0.347662099 0.7259950026 #> 589 0.4961458538 -0.2345845940 0.606748859 1.3908139548 #> 590 0.3164937916 0.1095979736 0.874649305 1.3544397157 #> 591 0.3380497008 -0.8024833497 0.261773133 1.9617006561 #> 592 1.0665751542 -0.0775564886 0.049889589 0.8019540157 #> 593 -0.5374398433 -0.7934627379 0.664968629 2.1177225395 #> 594 -0.6314629514 -0.0756081177 -0.082993659 0.7454231006 #> 595 -1.0560442250 0.5405547198 1.068572641 1.0120329497 #> 596 -0.5326743862 -0.2900671751 -0.811500487 -0.5712436457 #> 597 1.2863228674 0.5145789536 0.670557638 -0.2672647506 #> 598 0.0451731991 0.5359798380 -0.913268041 -2.0143420187 #> 599 -0.6952671335 0.9167701569 1.296589519 0.0969991237 #> 600 -2.7363006285 0.8750903881 1.341544384 -0.0889496301 #> 601 -0.8758952247 0.7624869355 1.868896989 1.4319349426 #> 602 0.5856062243 -0.7874949460 0.341749096 1.9132939272 #> 603 -0.9627503743 0.5122745815 0.267183382 -0.1321589636 #> 604 -0.4004473246 -0.5201327942 -0.569102764 -0.1176197285 #> 605 -0.9512244044 -0.7586787053 0.498967067 2.3052886341 #> 606 -0.4967838039 0.6304347512 -0.151588104 -1.1962975988 #> 607 -0.1693381631 -1.1538830384 -1.046646379 0.0999659471 #> 608 1.0366938162 2.0621762793 2.303472620 -0.5211401944 #> 609 0.7002720048 -1.2636568923 -1.632009761 0.4224003105 #> 610 -0.1266833485 0.4818373479 0.996164369 0.7321988059 #> 611 -2.0142889482 -0.5995118269 0.908322890 2.0221320575 #> 612 -1.5493455344 -0.5780376546 -0.530444702 -0.1611701799 #> 613 0.3923936965 0.5347873253 0.404909135 -0.4883024285 #> 614 0.8386451785 1.7248576839 1.713506421 -0.3519541475 #> 615 -0.6352838502 -1.5343419429 -1.271865187 0.5115024002 #> 616 0.6385819103 0.0941732392 0.282074129 0.1818724395 #> 617 0.2390371330 -0.4993828955 -1.178027552 -0.8780417978 #> 618 -0.6906692610 0.2680308484 1.174965724 1.2094065570 #> 619 -0.0420338677 1.9270788558 1.636393812 -1.1422986270 #> 620 0.2438808618 -1.7167411081 -1.684191471 1.2505777297 #> 621 1.3403313458 -0.6375432763 -1.495083628 -1.2495368455 #> 622 0.5933776897 -0.6417354929 -1.383198124 -0.5424497287 #> 623 -0.9054844650 -1.5823517743 -1.916929018 0.6338039583 #> 624 -0.8363539920 0.7292392503 -0.459239036 -2.2470600140 #> 625 1.0435909186 -0.6326780265 -0.986488214 -0.2389315239 #> 626 -1.6888842705 0.0906626829 1.138325376 1.4454500460 #> 627 -1.2103858466 -2.1717818435 -1.656641172 1.2892885841 #> 628 -0.2161294458 -0.4529931514 -1.202511724 -1.4088157346 #> 629 1.5501080042 0.9428268138 0.624794239 -0.6831640499 #> 630 -0.5092518713 -0.9818011534 -0.468948783 1.4566801204 #> 631 -0.2538721942 -1.1363627552 -1.223901623 0.3351313578 #> 632 -0.7363730604 0.6591487022 0.065980147 -1.0978451286 #> 633 0.6196165736 0.0688217354 0.337886694 0.5631528890 #> 634 -1.2576689270 -0.3227084778 0.275945726 0.7394384420 #> 635 1.1438301679 -0.2607950489 -0.489690886 -0.1797010911 #> 636 0.6665801522 0.2752522039 -0.575208101 -0.8628676103 #> 637 -0.7779399411 -0.2957134073 -0.676270888 -0.1543121439 #> 638 -0.3174705628 -0.2876014994 -0.195753221 -0.0147844657 #> 639 0.0864920385 -0.1114925948 0.064005245 0.4322262735 #> 640 0.6248883181 -0.1252694088 -1.130721335 -1.7468014202 #> 641 0.5787570894 -0.3241654155 -1.438406227 -0.9848176167 #> 642 -0.0594945663 0.4681470721 0.254021136 -0.0988252739 #> 643 -0.4978049328 -0.1814643368 0.676679999 1.4778311711 #> 644 1.5987086390 0.9752029619 1.301187825 -0.2199095282 #> 645 0.7507220816 0.9141130936 0.974713443 -0.4262853793 #> 646 -1.9242827544 0.6372644734 1.385255851 0.3576828180 #> 647 -0.7556012249 -0.6707785611 -1.314285794 -1.0033586091 #> 648 0.6574948316 -1.4837559485 -1.198395666 1.4890065415 #> 649 -0.9284846394 -1.1943612132 -0.192686110 2.0598346952 #> 650 0.6881453583 -0.9472139562 -1.164422747 -0.0064190080 #> 651 -0.0737933303 1.3200024913 0.492163370 -1.4247363837 #> 652 -0.6375413001 -0.0117008853 0.705823144 1.2069505603 #> 653 -0.0072406190 -1.1453500912 -0.899155278 0.1120799746 #> 654 0.3579303035 -0.5713767399 -0.765019674 0.2104561359 #> 655 0.9975946589 0.0635987036 -0.741743976 -1.6419483739 #> 656 -1.4545079032 1.7352662912 1.927148459 0.2858529641 #> 657 0.4672351818 -0.5172862222 -0.536265374 -0.1625385985 #> 658 -1.2059303701 0.7636857751 0.841590751 0.4475778366 #> 659 -2.2392674216 0.1426994592 0.109498801 0.1611965206 #> 660 0.0761683182 -2.1800811254 -1.605700160 2.6222409273 #> 661 -0.7320082355 1.1865119188 1.803049669 0.3801084141 #> 662 -1.2613655111 1.5137301076 1.879506082 -0.0961759653 #> 663 0.4737278786 -0.1713876468 -0.471088131 -0.3741098931 #> 664 -0.4055584280 0.1204510495 1.102389391 1.0057796996 #> 665 -0.1096022712 1.8190858470 2.078385193 0.0490648166 #> 666 0.5912071964 0.1397610528 -0.088196608 -0.2876619846 #> 667 1.1641364546 -0.0757056626 0.159043797 0.2794683046 #> 668 -1.0857452902 0.7766007912 0.932163585 -0.1217333823 #> 669 1.9171136144 0.3087094123 -0.745446199 -1.6397749473 #> 670 0.0170002664 -1.6255035948 -1.085908303 1.6770147049 #> 671 -0.1679750862 0.2188835215 -0.725215446 -1.8860166588 #> 672 -0.0004898524 -0.9507636818 0.030599124 1.2803563216 #> 673 0.8560495538 -0.5626965983 -0.531928418 0.1590499599 #> 674 0.5039646392 -1.2135577566 -1.188876360 0.1476039136 #> 675 -2.2131237848 -0.3804082015 -0.494096219 0.0157931646 #> 676 0.0908022944 0.3255100808 1.088526216 0.8613148382 #> 677 -0.0901713832 -1.4184309801 -1.349146691 0.7339305209 #> 678 0.2559094371 -0.6603128583 -0.688647120 0.6402630094 #> 679 -0.1540698886 -1.4214172334 -1.050288731 0.9220425830 #> 680 1.5290031321 -0.1950978921 -0.206804667 0.0366715348 #> 681 -0.4022421474 -0.0050045210 0.639909589 0.6613532981 #> 682 -0.5542071076 0.9275224175 0.005314208 -1.5498301457 #> 683 0.5748780270 -0.5386533707 -0.452243249 0.1848732614 #> 684 -1.2624707495 -2.8106352440 -2.658669605 1.3234982252 #> 685 0.3633090782 0.6153150646 -0.086036227 -1.7243890623 #> 686 -0.4040273435 -0.4371513441 -0.673780076 -0.3948567500 #> 687 1.9498044688 -1.9841562894 -1.777805084 1.1746266622 #> 688 -0.6932043386 1.4678731670 1.348024719 -0.0625178832 #> 689 -0.7801379943 0.1922283623 0.010336005 -0.4056508624 #> 690 1.3452618806 -0.0408907299 0.378443518 0.2249384303 #> 691 -0.2943885488 -0.2383488361 0.188819961 0.8542047196 #> 692 -1.5717567614 0.9402004493 1.332366830 0.0305555848 #> 693 -2.1004821015 -1.2843292428 -1.472686158 -0.1106693781 #> 694 -0.8784274832 -0.7233297661 -0.434060198 0.8479727118 #> 695 -1.5129183778 -0.0405561668 -0.148710075 0.2020458706 #> 696 -1.1778306588 1.1600092724 0.853135724 -0.2167217853 #> 697 -0.8587384852 -1.9091341931 -1.566507222 0.9264831784 #> 698 -0.6340944306 0.7051015394 0.536705108 -0.3628398703 #> 699 -0.5538425324 0.6090848315 -0.143648880 -1.9533402716 #> 700 -0.3652173107 1.7243308216 1.399882950 -0.8847730330 #> 701 0.9380645706 -0.6681644882 -0.944637839 -0.4521769889 #> 702 -0.3886544525 -1.2359756154 -0.701395919 0.6828929636 #> 703 0.3122813603 0.6117717749 0.387678688 -0.5416466344 #> 704 -2.8786319712 1.5605366986 1.281749304 -0.8939656552 #> 705 -0.2115104500 -0.4446517237 0.194670403 1.5176599013 #> 706 -0.2715415174 -2.1365342190 -1.474615634 2.0679749134 #> 707 1.8209351661 -0.3440435296 -1.244210938 -0.7863259752 #> 708 -1.2155194238 -0.6786410592 -1.299201207 -0.8524052448 #> 709 1.0158802244 0.9132581515 0.774353026 -1.0777305053 #> 710 1.2420296448 0.0940418482 -0.183688685 -0.7128086666 #> 711 -1.4237357212 0.5896739735 -0.174977586 -1.1038959233 #> 712 1.6838773758 -0.6097834941 0.412420023 1.7219590383 #> 713 -0.6150120746 0.6032192875 0.551636787 -0.2861403953 #> 714 0.0345827061 -1.0395276531 -1.098235680 0.4486927611 #> 715 -0.2832630001 1.4049611140 1.268026140 -0.3914370025 #> 716 0.1545002475 0.2002355762 0.211822034 0.3048403977 #> 717 -1.0708844925 1.7471167559 1.010392562 -1.5973520043 #> 718 -0.6694954477 0.9363904231 1.221383798 0.0432159008 #> 719 -0.7457406705 1.6353906920 1.512354224 -0.8705524509 #> 720 -0.3943234952 1.1926616578 1.723850645 0.1862201853 #> 721 -1.0836001293 0.2551578604 1.731725264 1.9840885024 #> 722 1.5303626234 0.1650436006 1.094437083 1.1185693914 #> 723 -1.4271946678 -0.6275573210 -0.084302121 0.9110334145 #> 724 -0.9348800870 -0.4075511101 -0.238537516 0.2632035337 #> 725 -1.4210540159 -0.5525190422 -0.368755730 0.2104393511 #> 726 -1.4750597194 0.0740258369 0.377555626 -0.0518100604 #> 727 0.6015020275 -0.6952396229 0.205403034 1.4191190092 #> 728 0.3452907530 -0.1729181277 0.473108676 1.3076180945 #> 729 -0.3147620932 -0.2438576067 0.207279201 0.7527979105 #> 730 -0.6559068994 -1.3696012365 -0.436324210 1.4805027033 #> 731 0.1574756847 0.1305336600 -0.402340136 -1.1620121401 #> 732 0.4732917401 1.1876962205 1.388473689 0.0444150971 #> 733 0.7245961516 -1.5605547019 -1.447815104 0.4266102052 #> 734 0.7327227408 -1.3441438774 -1.135295476 0.5245190050 #> 735 1.1736736647 0.3374315004 0.047029596 -0.4261295135 #> 736 0.1405083868 0.4968418840 1.209248567 1.1283526792 #> 737 -2.5383954227 0.0614587021 0.072391778 0.4571298713 #> 738 -1.1738044838 -0.5128260873 -0.212904404 0.4893807088 #> 739 -1.7466658946 1.2176639125 1.101415169 -0.1077513421 #> 740 0.0983341298 -0.6159515894 -1.290189525 -0.6592716178 #> 741 -1.3684820029 -0.3180995386 0.175467609 0.7607304204 #> 742 1.6643770424 0.6733818362 0.294005626 -1.1656835664 #> 743 1.7382941087 0.3557662018 0.528786009 0.3937590286 #> 744 0.6128476306 -0.1121162100 -0.911360169 -1.3476938996 #> 745 -1.3703182602 -0.3019742783 -1.382649766 -1.1815596867 #> 746 -0.4590141175 0.2979306539 0.269864002 -0.4919737107 #> 747 -0.9368951523 -1.6650059076 -0.596317753 1.6599359653 #> 748 -0.5618429801 -0.9541205404 -0.795756235 0.5547828745 #> 749 -1.3186748349 1.4930732510 1.535849730 -0.5782595810 #> 750 -0.5910729029 -1.0607126586 -0.349196081 1.4349338392 #> 751 0.8772163560 0.6422399667 0.028027431 -1.4548577113 #> 752 1.9414024711 2.0489002445 1.800250402 -1.1808979936 #> 753 -0.2252302817 0.0702681641 0.645210798 1.2543749153 #> 754 -0.1988164646 0.5486887607 0.444806045 -0.0908102542 #> 755 -2.2347950031 0.3385869469 -0.211156719 -1.0388754295 #> 756 -0.4419874235 1.1413519376 0.762348427 -0.7869199465 #> 757 -0.9300438139 0.0799854609 -0.230864934 -0.4518375999 #> 758 1.5427556172 1.0619417078 0.715928407 -1.1323897776 #> 759 0.8141802414 0.3885047425 1.246753501 1.3405466314 #> 760 -0.4850482031 -2.5092919869 -2.613137098 0.4286810221 #> 761 -1.7911321464 -1.3757647841 -1.403690704 -0.1158081964 #> 762 0.2552024791 -0.6170574435 -0.940809538 -0.3130405144 #> 763 -2.0301790965 0.0723832204 0.267741855 -0.3048146667 #> 764 1.7792536970 -0.7763482923 -0.044160373 1.2583076860 #> 765 1.7536000605 -1.0131957490 -0.357635770 1.5622384207 #> 766 0.5932820209 -0.8846594588 -0.559528638 0.9299626396 #> 767 1.0750088042 -0.0795076836 -0.591101412 -0.3845543714 #> 768 1.0266259614 -0.1665758940 -0.237733589 0.3359773395 #> 769 1.8838743666 0.5969922946 0.711060822 0.3523694155 #> 770 -1.1929078766 0.6742725587 -0.587503819 -2.4587582359 #> 771 0.1732576741 -0.5168705078 -0.753063904 -0.5101097308 #> 772 -1.9013221756 -0.1755818258 -0.407408877 -0.5927905910 #> 773 -0.4490646136 -0.4058911142 0.126903798 1.2596924689 #> 774 0.0449848225 0.4470448722 -0.071841376 -1.3047358838 #> 775 1.5480093198 0.1648328848 0.229152426 -0.1178934138 #> 776 0.4533917260 -0.3311256703 -0.566735447 -0.6227409419 #> 777 -0.7664692649 -0.5767755906 -0.562657412 0.0623121260 #> 778 -0.0097612207 0.1411269815 0.483088120 0.6899912062 #> 779 -0.4556498995 -0.8367992472 -0.668926255 0.3989580327 #> 780 -0.7176960976 -0.1567775988 -0.325636629 -0.4023945104 #> 781 -0.9082690526 -0.1501156508 0.283500046 0.6950326997 #> 782 1.3621695818 2.4616947658 2.044579163 -0.7207314114 #> 783 0.6613768337 0.5510749737 1.234486041 0.9465005888 #> 784 -0.5189683829 -0.6123261485 -0.275076919 0.6932369584 #> 785 -1.4259202467 -1.6630024274 -1.738985134 -0.0938968856 #> 786 -1.1513141263 -0.7170150432 -0.912500204 -0.2061299796 #> 787 2.1121673244 -0.9219781412 -0.790497068 0.7961278523 #> 788 -0.6389747397 -0.6677667513 -1.587622046 -1.4320268444 #> 789 -0.6917576180 -1.3078628192 -1.755805533 -0.5159557415 #> 790 0.7055479276 -1.2679117775 -0.648310275 1.1583485023 #> 791 0.7296099146 1.0009617957 0.697399678 -0.8626039748 #> 792 -0.2807591606 -0.1715994390 -0.474466607 -1.0114773826 #> 793 0.5208296262 -0.2012131572 -0.230770942 0.2899214498 #> 794 0.2984108629 -0.6484486229 -0.688540956 0.6120314610 #> 795 1.0419865121 -1.8474004685 -1.675974156 0.5564894681 #> 796 -1.3908030830 0.8035005041 0.286180252 -1.7340490575 #> 797 0.4195653155 -1.5458376590 -1.531649386 0.8146693973 #> 798 1.4284384294 0.7394024048 -0.263897236 -1.5664717965 #> 799 0.6243304040 -0.2305038316 0.554058330 1.6699897267 #> 800 0.4946065489 2.3445544521 1.870087946 -1.1967339897 #> 801 -0.2824653374 -0.7366760203 -0.318097300 0.7652781478 #> 802 0.1313703439 -0.8621065364 -0.519424016 0.4721759154 #> 803 -0.3273622739 -2.3174256700 -1.957967721 1.2337201257 #> 804 -0.7480279864 -1.2412483335 -0.279471901 1.5963747945 #> 805 -0.9272512850 1.0786583086 0.761496444 -0.7252813910 #> 806 0.7084775046 -2.4548442508 -3.628313100 -0.7454158655 #> 807 0.4439906679 0.9909901115 1.012125448 -0.2695933269 #> 808 0.3972109201 -0.0417435169 -0.325020715 -0.6278572727 #> 809 1.1010864990 1.6755809265 1.372531635 -1.1357838675 #> 810 1.5495821209 -1.1358316257 -0.810165079 0.4424896119 #> 811 -1.0185613030 -1.0144391009 -0.759725728 0.7043249735 #> 812 0.4416434813 1.7736246291 0.749401592 -2.4286608179 #> 813 0.5038520562 0.0788402858 0.600572433 0.5154254609 #> 814 -0.8825714892 0.8589226302 0.325846790 -1.4037060630 #> 815 -0.3179779669 0.5531742042 0.704613210 0.2092886342 #> 816 -1.4997620707 1.3205270707 0.671195636 -1.2144674113 #> 817 0.7964842432 0.8354260126 0.182167192 -1.1059150703 #> 818 -1.3291084191 2.7570257861 2.702697678 -0.3506612548 #> 819 1.4866019618 -0.7563113191 -0.859906243 0.1927489685 #> 820 0.5820567072 -1.0499269653 -0.239170025 1.4993575153 #> 821 -0.7061669535 0.0865956414 0.200690590 0.3420561735 #> 822 1.5735533431 1.2606061690 0.972263674 -0.5290902000 #> 823 -1.6038536585 0.9062336500 0.448471640 -0.5063419930 #> 824 0.8494854303 -1.0558514524 -1.348198749 -0.1856978959 #> 825 1.2463696637 -0.0996032204 -0.836291364 -1.6193047126 #> 826 0.3244265031 -2.1858317298 -1.229468726 2.2056510444 #> 827 1.3728210268 0.7152008431 0.009959350 -1.0752309665 #> 828 -1.5916222753 1.0469488287 1.391879926 -0.1932908952 #> 829 1.7360131061 0.2448835577 -0.649019210 -1.7408968212 #> 830 -0.0060375295 -0.1848613300 0.183672525 0.4172360029 #> 831 -1.4563188435 -1.5108957550 -2.194099855 -1.1740927542 #> 832 -0.0420660207 0.2537061766 -0.407922534 -0.9505314871 #> 833 0.9628787738 -0.8937053937 -0.484679152 0.8282125233 #> 834 -0.6548214043 1.8206252470 1.515583414 -0.6111362964 #> 835 -0.2300650596 -1.0441826251 -0.911280789 0.2881066394 #> 836 -0.5137799601 -0.9779747797 -0.726227764 0.2750370703 #> 837 0.5302170356 0.6320324782 0.031084272 -0.9335748166 #> 838 2.2130514427 1.7424035860 1.309458225 -1.3979211388 #> 839 -0.7015524598 -1.4507402517 -1.520473564 0.4244247419 #> 840 1.2693293416 0.6392141181 1.336446257 0.7898404988 #> 841 -0.4831102690 0.7446660430 1.288373819 0.1796532118 #> 842 0.2887953308 -0.7710383350 -0.924091244 0.4007222659 #> 843 -1.6835705400 0.7919072952 1.047587066 -0.2503956257 #> 844 -1.1936059622 -1.0358134455 -1.006124067 0.2227655879 #> 845 1.0394144247 -0.9762313292 -0.858704669 0.6983826136 #> 846 0.2884344093 -0.6252740325 0.346877263 1.8229745025 #> 847 0.4072897555 -0.1171133850 0.451473714 0.6034868006 #> 848 0.4286372186 -0.8283062627 -0.887288241 0.0108428832 #> 849 -2.2061380527 0.0313344034 0.423583538 0.9884434226 #> 850 -0.7926383090 -1.2841961954 -0.615988436 0.8239554969 #> 851 0.8507887064 -0.5460128730 -1.476084147 -1.6387697879 #> 852 0.8808798516 0.9630566397 1.092435173 0.1218794873 #> 853 -0.3128484540 0.7785429286 0.612289072 -0.4735490723 #> 854 0.2686795324 0.3146427576 0.468891299 0.3298819834 #> 855 -0.6163263202 -0.7551812721 -0.167303398 1.5104659520 #> 856 2.1797557099 -1.1282012324 -0.863036222 0.7244408110 #> 857 -0.2730971962 2.1030337915 1.600279653 -1.7222169503 #> 858 -0.2263926228 -0.3693750422 0.679805577 1.5903476996 #> 859 -0.7633529632 -1.4666474093 -1.346359966 0.3483337597 #> 860 -1.3688573668 -0.7161702306 -0.395538306 0.8160923023 #> 861 -0.1288171773 0.1996713073 0.767091896 0.6646674252 #> 862 -0.3083049975 -1.0616047886 -1.772572135 -0.0443515046 #> 863 -0.1016505588 -1.5265829061 -1.324467466 1.0585615601 #> 864 -0.1620229996 -1.1548553672 -1.243338312 0.5463070649 #> 865 -0.4707424886 -2.5690630034 -2.469974880 0.7838326916 #> 866 0.8661921528 0.3961274681 0.679270493 0.3029985638 #> 867 0.3028708896 -0.0094293454 0.118892568 0.1746146559 #> 868 -0.8111268971 0.9684982891 0.111346330 -1.0379809937 #> 869 0.2851594402 1.4498009044 0.867231043 -1.4297744700 #> 870 -0.2251104022 2.7578751077 2.181331359 -1.8913893379 #> 871 0.1368509725 3.0693999644 1.936068677 -2.2138115625 #> 872 0.4985284119 -0.0719259483 0.609127607 1.4429568117 #> 873 -2.0106137323 1.1712080092 1.643143296 1.0533661269 #> 874 -0.1829939793 -2.0836991515 -2.062435291 0.3976284881 #> 875 0.4124240196 -0.0935285905 -0.010163707 0.1029853685 #> 876 -0.9335673396 0.2874848806 -0.721668978 -1.9596475755 #> 877 -0.2092973704 -0.7775340297 -0.622405469 0.5827398665 #> 878 -0.0267124000 0.5805054737 0.597145121 0.3269908977 #> 879 -1.8352046585 1.1539049328 1.760503855 1.1623150623 #> 880 -0.0456786470 -0.8569683150 -1.048150555 -0.2295626443 #> 881 -1.2060442957 -0.6979701377 -1.070950053 -0.6153376535 #> 882 0.9789629182 1.5620044030 1.049713701 -1.1520624067 #> 883 0.1010927119 0.7296496270 1.236504864 0.4627969810 #> 884 -0.1793136345 0.8798665483 0.876778375 0.0761608563 #> 885 0.3112573440 -0.5946462142 -1.051424527 -0.4873484607 #> 886 0.5437623223 0.2983138616 0.377306008 0.5938705772 #> 887 -2.0398248523 -0.1690534778 -1.253473923 -1.2669718763 #> 888 0.1631469085 1.0393516766 2.099425192 1.2687901252 #> 889 0.5996762529 0.4564583027 -0.966419850 -2.5151274499 #> 890 0.4407786448 -0.3083160055 0.134900472 0.3608898227 #> 891 0.1837076561 -1.0553439477 -0.585816897 1.3945187859 #> 892 -2.1923669386 0.5159484295 0.403855111 -0.3979677923 #> 893 0.6073626851 -1.5219919644 -1.664031058 0.6972003463 #> 894 -1.3194838376 -1.5963982717 -1.056968012 1.4982818932 #> 895 -0.6656003989 0.9811794915 -0.327045817 -2.4489032260 #> 896 1.2892205466 -0.0270730880 0.283903898 0.1872387106 #> 897 0.4389043238 -1.2490850331 -0.354510036 2.0011448732 #> 898 1.8108836996 0.4963849558 0.166791512 -1.1747283936 #> 899 -0.4311966657 0.1019209507 -0.071849951 -0.4337880369 #> 900 0.4708149510 -0.1385399887 -0.304215015 -0.3230860979 #> 901 0.5573213793 -1.0489214811 -0.734034926 0.2004882163 #> 902 0.1855787789 0.4909039493 2.002255975 2.7573648256 #> 903 0.9018878242 -0.4215955600 -0.764166292 -0.2712003376 #> 904 -0.2130973883 -0.9185198717 -0.737543323 0.5473558157 #> 905 -0.0474423429 -0.1434392857 -0.104374862 -0.3134263383 #> 906 -1.2946449292 0.5140625698 0.919450811 0.9352097011 #> 907 -1.9924701889 0.1005192336 0.212437284 0.4029697848 #> 908 -0.8873021182 0.4736888266 -0.067848813 -1.4028619661 #> 909 -1.2745557355 1.4149802806 1.487309021 -0.0774356636 #> 910 -1.3683707437 1.9522230980 1.350987610 -1.7320542997 #> 911 -0.1967469250 -0.0056673718 -0.111794048 -0.0841171955 #> 912 0.5517750618 0.9022387504 0.723668436 -0.5354535754 #> 913 0.0782811899 0.6606829694 0.195393643 -0.2530835851 #> 914 -0.7508076748 0.5284569297 0.772694665 -0.4506761376 #> 915 -0.3396081941 -0.0981753841 -0.198752924 0.3614229608 #> 916 0.2609764110 1.2849329712 1.142301189 -0.6414186638 #> 917 0.2524393073 -0.9671571750 -0.704052197 0.6298719151 #> 918 0.0630523056 0.0042374056 0.100350166 0.0640418343 #> 919 0.6678230148 -1.0407229306 -0.683698164 0.6521823892 #> 920 0.2433243279 0.4493970172 0.483234134 -0.1217209646 #> 921 -1.6970964549 -2.5575885885 -2.713300120 0.5700121170 #> 922 0.3441936961 1.2156798605 0.701546511 -1.1476598902 #> 923 0.6455136434 0.6300481445 0.115451338 -0.2524077901 #> 924 -0.7089728864 -0.3736422768 -0.012939102 0.2212622616 #> 925 -0.4841715206 -1.6788117326 -1.035312799 1.7081416116 #> 926 -1.2635203716 0.9556309214 1.213375825 0.1190602660 #> 927 -0.7213123189 -0.0527329796 0.413122635 0.5015636087 #> 928 -1.4294247702 0.4427589803 0.323869075 -0.2619797587 #> 929 0.9822535394 -0.6434358983 -1.115309830 -1.1209537650 #> 930 -1.6579670226 1.3713889518 1.400622005 -0.2174936721 #> 931 -1.4205875904 -0.3087522107 0.102247784 0.4273958770 #> 932 0.4310844605 -0.7607476069 -0.789561338 0.6681964789 #> 933 0.7762627740 -0.0486183912 -0.590502739 -1.0326233077 #> 934 0.9666903035 0.2790414969 0.171449744 -0.1562848425 #> 935 -1.2634211792 -1.0517956415 -0.551255788 1.0536575436 #> 936 0.0973634539 -1.1582368045 -0.956025035 0.8588950576 #> 937 -1.0858533575 -0.6844814443 -0.007352250 1.2403270766 #> 938 1.0227450297 0.7303251580 0.701861787 -0.4109947417 #> 939 -0.2352751770 -0.3585984685 0.034527568 1.0306089414 #> 940 0.4258553370 1.4705095719 1.644539372 -0.2400348882 #> 941 -0.0557223973 0.0333765437 0.346276446 0.8844330078 #> 942 0.1401301413 -0.4758220528 -0.302618019 0.2300095821 #> 943 0.0465151576 -2.0098283303 -1.535085260 1.1564979653 #> 944 -0.1891369786 0.0040558621 -0.957295024 -1.8457170669 #> 945 1.4929759134 1.0331332306 0.213463458 -1.3803621307 #> 946 -0.6304366043 0.7287388844 0.473908404 -0.2323081500 #> 947 0.8768511704 -0.7559963745 0.586279159 2.2840924999 #> 948 -1.3085993802 0.7004715444 0.564710074 -0.1349677926 #> 949 -0.1109679775 0.5358508097 1.087650284 1.3880163565 #> 950 0.6204873652 1.5928530207 1.896645677 -0.2941840516 #> 951 -0.4601902603 -0.4330332317 0.128301454 0.4213490900 #> 952 -0.3915820370 0.0338751543 -0.728645801 -0.8319587388 #> 953 -1.0408651683 0.1707294872 -0.213702489 -0.9693475273 #> 954 -0.0367321540 -1.0286598793 -1.889471299 -0.9507735576 #> 955 0.4346744743 2.4295851919 2.790949126 0.2900830442 #> 956 -0.7876451846 0.5218989013 0.618789753 0.0206063413 #> 957 0.3384012758 1.3221983709 1.069794857 -0.6626404882 #> 958 -1.3831608621 -0.5426091838 -1.045239345 -0.7939578030 #> 959 -0.2749337495 -2.7681164127 -2.513734334 1.5236256459 #> 960 0.6579269250 0.2974833000 0.101918446 -0.4342141292 #> 961 -0.6771577902 0.2543842901 0.493471278 0.1401768508 #> 962 0.9007992072 1.3917072717 0.342982123 -1.9895021932 #> 963 0.4458374094 0.1018223777 0.017999544 -0.2570169573 #> 964 -0.1693277702 1.4433207655 2.081982558 0.4934128210 #> 965 -0.7352733505 -0.2625306165 -0.138356828 0.7671370027 #> 966 0.9351357739 1.8758047902 1.208696263 -1.7178642320 #> 967 -1.0515867373 0.8191829437 1.175206265 0.1421878663 #> 968 -0.9541786695 0.0290683759 0.460697730 0.6895890374 #> 969 1.2742100302 0.7234406338 -0.163889281 -1.7197893491 #> 970 -1.1532097322 0.4497584586 0.435971450 -0.2672792190 #> 971 1.2856473038 0.0842571579 -0.248254038 -0.9375633407 #> 972 0.1367775527 1.5500171163 0.756371746 -1.6851122123 #> 973 -1.4722725972 -1.1749441859 -1.765852547 -0.4805081685 #> 974 0.8613533736 -0.3570904526 -1.252350408 -0.9302867854 #> 975 -0.0899002219 -0.3006434466 -0.119820214 0.4990886155 #> 976 0.1296033005 0.7132651022 0.593727036 -0.3523063514 #> 977 -0.3851816270 -0.2791414087 0.261212304 0.9912653998 #> 978 -1.5026312506 1.0374722481 0.629147414 -0.4988912716 #> 979 -0.5336793021 -0.1596112805 -0.222750332 0.3315287052 #> 980 2.0509146991 0.3799065213 0.519045354 0.5244240378 #> 981 0.9393312575 -1.8652101656 -1.743940546 0.7258215156 #> 982 1.0424927447 -1.6018147985 -1.630369745 0.3861080698 #> 983 -0.2937500560 -1.5110521403 -1.619767144 -0.2090171634 #> 984 -0.4271312040 0.7398844848 0.135389180 -1.4893692036 #> 985 -0.9575948113 1.7025196105 0.967290422 -2.1670858811 #> 986 1.0613645759 0.7478613689 1.206752528 0.4456106606 #> 987 -0.8173118759 -0.1079795094 0.197474110 0.6242578430 #> 988 -0.3990432618 -0.1733378125 0.193949862 0.7589317976 #> 989 -0.7261482843 -0.1671368174 -0.017705953 0.3927015008 #> 990 -0.7101145456 0.3014745648 -0.230666864 -0.7806266277 #> 991 0.7997868118 1.1995363904 1.507301299 -0.0789141760 #> 992 0.3212550843 -0.0636215169 0.063647318 0.9408785566 #> 993 -0.5356825003 0.3206007781 1.166512790 1.7034893053 #> 994 0.6859275918 0.2471914430 0.123476848 -0.5304519446 #> 995 0.1962488287 -0.1216908057 -0.198403881 0.0002219803 #> 996 2.4831838168 -0.0782187395 1.473723808 1.8571316049 #> 997 0.5747718967 -2.8189659519 -2.905952605 0.4046344439 #> 998 -1.0648583323 0.1998847209 -0.517541832 -0.9332636902 #> 999 -0.2312225216 -0.2999658454 -0.204561509 -0.1774115591 #> 1000 0.5015486388 0.7851524817 0.724885412 -0.2499866497"},{"path":"http://svmiller.com/reference/corvectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Create multivariate data by permutation — corvectors","title":"Create multivariate data by permutation — corvectors","text":"corvectors() function obtain multivariate dataset specifying relation specified variables.","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create multivariate data by permutation — corvectors","text":"","code":"corvectors( data, corm, tol = 0.005, conv = 10000, cores = 2, splitsize = 1000, verbose = FALSE, seed )"},{"path":"http://svmiller.com/reference/corvectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create multivariate data by permutation — corvectors","text":"data data matrix containing data corm value containing desired correlation vector data matrix containing desired correlations tol single value vector tolerances length ncol(data) - 1. default 0.005 conv maximum iterations allowed. Defaults 1000. cores number cores used parallel computing splitsize size use splitting data verbose Logical statement. Default FALSE seed optional seed set","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create multivariate data by permutation — corvectors","text":"corvectors() returns matrix given specified multivariate relation.","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create multivariate data by permutation — corvectors","text":"liberally copy-pasted van Kooten Vink's wonderful---longer-supported correlate package. call correlate() package, opt corvectors() .","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create multivariate data by permutation — corvectors","text":"Pascal van Kooten Gerko Vink","code":""},{"path":"http://svmiller.com/reference/corvectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create multivariate data by permutation — corvectors","text":"","code":"if (FALSE) { # \\dontrun{ set.seed(8675309) library(tibble) # bivariate example, start with zero correlation as_tibble(data.frame(corvectors(replicate(2, rnorm(100)), .5))) # multivariate example as_tibble(data.frame(corvectors(replicate(4, rnorm(100)), c(.5, .6, .7)))) } # }"},{"path":"http://svmiller.com/reference/db_lselect.html","id":null,"dir":"Reference","previous_headings":"","what":"Lazily select variables from multiple tables in a relational database — db_lselect","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"db_lselect() allows select variables multiple tables SQL database. returns lazy query combines variables together one data frame (tibble). user can choose run collect() query see fit.","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"","code":"db_lselect(.data, connection, vars)"},{"path":"http://svmiller.com/reference/db_lselect.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":".data character vector tables relational database connection name connection object vars variables (entered class \"character\") select tables database","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"Assuming particular structure database, function returns combined table including requested variables tables listed data character vector. returned table attributes inherited dplyr interfaces SQL, allowing user extract information query (e.g. show_query()).","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"wrapper function purrr dplyr heavy lifting. tables database declared character (character vector). variables select also declared character (character vector), wrapped one_of() function within select() dplyr.","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"Miller, Steven V. 2020. \"Clever Uses Relational (SQL) Databases Store Wider Data (Assistance dplyr purrr)\" http://svmiller.com/blog/2020/11/smarter-ways--store--wide-data--sql-magic-purrr/","code":""},{"path":"http://svmiller.com/reference/db_lselect.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Lazily select variables from multiple tables in a relational database — db_lselect","text":"","code":"# \\donttest{ library(DBI) library(RSQLite) library(dplyr) #> #> Attaching package: ‘dplyr’ #> The following object is masked from ‘package:stevemisc’: #> #> tbl_df #> The following objects are masked from ‘package:stats’: #> #> filter, lag #> The following objects are masked from ‘package:base’: #> #> intersect, setdiff, setequal, union library(dbplyr) #> #> Attaching package: ‘dbplyr’ #> The following objects are masked from ‘package:dplyr’: #> #> ident, sql set.seed(8675309) A <- data.frame(uid = c(1:10), a = rnorm(10), b = sample(letters, 10), c = rbinom(10, 1, .5)) B <- data.frame(uid = c(11:20), a = rnorm(10), b = sample(letters, 10), c = rbinom(10, 1, .5)) C <- data.frame(uid = c(21:30), a = rnorm(10), b = sample(letters, 10), c = rbinom(10, 1, .5), d = rnorm(10)) con <- dbConnect(SQLite(), \":memory:\") copy_to(con, A, \"A\", temporary=FALSE) copy_to(con, B, \"B\", temporary=FALSE) copy_to(con, C, \"C\", temporary=FALSE) # This returns no warning because columns \"a\" and \"b\" are in all tables c(\"A\", \"B\", \"C\") %>% db_lselect(con, c(\"uid\", \"a\", \"b\")) #> # Source: SQL [?? x 3] #> # Database: sqlite 3.41.2 [:memory:] #> uid a b #> #> 1 1 -0.997 f #> 2 2 0.722 z #> 3 3 -0.617 y #> 4 4 2.03 x #> 5 5 1.07 c #> 6 6 0.987 p #> 7 7 0.0275 e #> 8 8 0.673 i #> 9 9 0.572 o #> 10 10 0.904 n #> # ℹ more rows # This returns two warnings because column \"d\" is not in 2 of 3 tables. # ^ this is by design. It'll inform the user about data availability. c(\"A\", \"B\", \"C\") %>% db_lselect(con, c(\"uid\", \"a\", \"b\", \"d\")) #> Warning: Unknown columns: `d` #> Warning: Unknown columns: `d` #> # Source: SQL [?? x 4] #> # Database: sqlite 3.41.2 [:memory:] #> uid a b d #> #> 1 1 -0.997 f NA #> 2 2 0.722 z NA #> 3 3 -0.617 y NA #> 4 4 2.03 x NA #> 5 5 1.07 c NA #> 6 6 0.987 p NA #> 7 7 0.0275 e NA #> 8 8 0.673 i NA #> 9 9 0.572 o NA #> 10 10 0.904 n NA #> # ℹ more rows dbDisconnect(con) # }"},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":null,"dir":"Reference","previous_headings":"","what":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"data illustrate labeled data process get_var_info() package.","code":""},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"","code":"ess9_labelled"},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"data frame 109 observations following 4 variables. essround numeric constant edition another numeric constant cntry character vector (label) country data netusoft numeric vector (label) self-reported internet consumption respondent","code":""},{"path":"http://svmiller.com/reference/ess9_labelled.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Some Labeled Data in the European Social Survey (Round 9) — ess9_labelled","text":"Data condensed summaries raw data. amount every unique combination country self-reported internet consumption. data illustrate get_var_info() function package.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":null,"dir":"Reference","previous_headings":"","what":"Reorganize a factor after ","title":"Reorganize a factor after ","text":"fct_reorg() forcats hack reorganizes factor re-leveling . situationally useful coefficient plots years.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reorganize a factor after ","text":"","code":"fct_reorg(fac, ...)"},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reorganize a factor after ","text":"fac character factor vector ... optional parameters supplied forcats functions.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reorganize a factor after ","text":"function takes character factor vector first re-levels re-coding certain values. end result factor.","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reorganize a factor after ","text":"Solution comes way issue Github: https://github.com/tidyverse/forcats/issues/45","code":""},{"path":"http://svmiller.com/reference/fct_reorg.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reorganize a factor after ","text":"","code":"x<-factor(c(\"a\",\"b\",\"c\")) fct_reorg(x, B=\"b\", C=\"c\") #> [1] a B C #> Levels: B C a"},{"path":"http://svmiller.com/reference/filter_refs.html","id":null,"dir":"Reference","previous_headings":"","what":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"filter_refs() convenience function wrote filtering data frame citations returning entries valid .bib entry (character vector). wrote easily passing citations print_refs() function also included package.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"","code":"filter_refs(bibdat, criteria, type = \"bibtexkey\")"},{"path":"http://svmiller.com/reference/filter_refs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"bibdat data frame citations, like one created bib2df package criteria criteria, specified character vector, filter data frame citations type particular type citation entry filter. Defaults \"bibtexkey\" (filters based column unique citation keys). type == \"year\", function filters character vector years.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"filter_refs() takes data frame citations, like one created bib2df package, returns character vector (amounting valid .bib entry) citations user wants. can easily passed print_refs() function also included package.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"filter_refs() assumes familiarity BibTeX, .bib entries, depends bib2df package.","code":""},{"path":"http://svmiller.com/reference/filter_refs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Filter a Data Frame of Citations and Return the Entries as a Character — filter_refs","text":"","code":"# Based on `stevepubs` configuration, filter on `BIBTEXKEY` where # the citation key matches one of these. filter_refs(stevepubs, c(\"miller2017etst\", \"miller2017etjc\", \"miller2013tdpi\")) #> @ARTICLE{miller2013tdpi, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Journal of Peace Research}, #> NUMBER = {6}, #> PAGES = {677--690}, #> TITLE = {Territorial Disputes and the Politics of Individual Well-Being}, #> VOLUME = {50}, #> YEAR = {2013}} #> #> @ARTICLE{miller2017etjc, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Research Quarterly}, #> NUMBER = {4}, #> PAGES = {790--802}, #> TITLE = {The Effect of Terrorism on Judicial Confidence}, #> VOLUME = {70}, #> YEAR = {2017}} #> #> @ARTICLE{miller2017etst, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Behavior}, #> NUMBER = {2}, #> PAGES = {457--478}, #> TITLE = {Economic Threats or Societal Turmoil? Understanding Preferences for Authoritarian Political Systems}, #> VOLUME = {39}, #> YEAR = {2017}} #> # Based on `stevepubs` configuration, filter on `YEAR` where # the publication year is 2017, 2018, 2019, 2020, or 2021. filter_refs(stevepubs, c(2017:2021), type = \"year\") #> @ARTICLE{curtismiller2021snp, #> AUTHOR = {K. Amber Curtis and Steven V. Miller}, #> JOURNAL = {European Union Politics}, #> NUMBER = {2}, #> PAGES = {202--26}, #> TITLE = {A (Supra)Nationalist Personality? The Big Five's Effects on Political-Territorial Identification}, #> VOLUME = {22}, #> YEAR = {2021}} #> #> @ARTICLE{gibleretal2020icm, #> AUTHOR = {Douglas M. Gibler and Steven V. Miller and Erin K. Little}, #> JOURNAL = {International Studies Quarterly}, #> NUMBER = {2}, #> PAGES = {476--479}, #> TITLE = {The Importance of Correct Measurement}, #> VOLUME = {64}, #> YEAR = {2020}} #> #> @ARTICLE{miller2017etjc, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Research Quarterly}, #> NUMBER = {4}, #> PAGES = {790--802}, #> TITLE = {The Effect of Terrorism on Judicial Confidence}, #> VOLUME = {70}, #> YEAR = {2017}} #> #> @ARTICLE{miller2017etst, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Political Behavior}, #> NUMBER = {2}, #> PAGES = {457--478}, #> TITLE = {Economic Threats or Societal Turmoil? Understanding Preferences for Authoritarian Political Systems}, #> VOLUME = {39}, #> YEAR = {2017}} #> #> @ARTICLE{miller2017ieea, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Conflict Management and Peace Science}, #> NUMBER = {5}, #> PAGES = {526--545}, #> TITLE = {Individual-Level Expectations of Executive Authority under Territorial Threat}, #> VOLUME = {34}, #> YEAR = {2017}} #> #> @ARTICLE{miller2018etttc, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Peace Economics, Peace Science and Public Policy}, #> NUMBER = {1}, #> TITLE = {External Territorial Threats and Tolerance of Corruption: A Private/Government Distinction}, #> VOLUME = {24}, #> YEAR = {2018}, #> DOI = {10.1515/peps-2017-0043}} #> #> @ARTICLE{miller2019wata, #> AUTHOR = {Steven V. Miller}, #> JOURNAL = {Social Science Quarterly}, #> NUMBER = {1}, #> PAGES = {272--288}, #> TITLE = {What Americans Think About Gun Control: Evidence from the General Social Survey, 1972-2016}, #> VOLUME = {100}, #> YEAR = {2019}} #> #> @ARTICLE{millerdavis2020ewsp, #> AUTHOR = {Steven V. Miller and Nicholas T. Davis}, #> JOURNAL = {Journal of Race, Ethnicity, and Politics}, #> NUMBER = {2}, #> PAGES = {334--351}, #> TITLE = {The Effect of White Social Prejudice on Support for American Democracy}, #> VOLUME = {6}, #> YEAR = {2021}} #> #> @INCOLLECTION{milleretal2020gtc, #> AUTHOR = {Steven V. Miller and Jaroslav Tir and John A. Vasquez}, #> BOOKTITLE = {Oxford Research Encyclopedia of International Studies}, #> PUBLISHER = {Oxford University Press}, #> TITLE = {Geography, Territory, and Conflict}, #> YEAR = {2020}, #> DOI = {10.1093/acrefore/9780190846626.013.320}} #>"},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":null,"dir":"Reference","previous_headings":"","what":"French Leader-Years, 1874-2015 — fra_leaderyears","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"data generated peacesciencer French leader-years 1874 2015. going use data stress-testing calculation -called \"peace spells\" data decidedly imbalanced, .","code":""},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"","code":"fra_leaderyears"},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"data frame 255 observations following 10 variables. obsid unique observation ID Archigos data ccode Correlates War state code France (220) leader name—typically last name—leader year observation year leader startdate start date leader's period office enddate end date leader's period office gmlmidongoing ongoing inter-state dispute leader? gmlmidonset new inter-state dispute onset leader? gmlmidongoing_init ongoing inter-state dispute leader leader initiated? gmlmidonset_init new inter-state dispute onset leader leader initiated?","code":""},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"Data generated development version (scheduled release v. 0.7) peacesciencer. Conflict data come GML MID data (v. 2.2.1). Leader data come Archigos (v. 4.1).","code":""},{"path":"http://svmiller.com/reference/fra_leaderyears.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"French Leader-Years, 1874-2015 — fra_leaderyears","text":"Goemans, Henk E., Kristian Skrede Gleditsch, Giacomo Chiozza. 2009. \"Introducing Archigos: Dataset Political Leaders\" Journal Peace Research 46(2): 269–83. Gibler, Douglas M., Steven V. Miller, Erin K. Little. 2016. “Analysis Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Simulations from a Model Object (with New Data) — get_sims","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"get_sims() function simulate quantities interest multivariate normal distribution \"new data\" regression model.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"","code":"get_sims(model, newdata, nsim, seed)"},{"path":"http://svmiller.com/reference/get_sims.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"model model object newdata data frame quantities interest simulated nsim Number simulations run seed optional seed set","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"get_sims() returns data frame (tibble) quantities interest identifying information particular simulation number.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"() flexible function takes merMod object (estimated lme4, blme, etc.) lm glm object generates quantities interest paired new data observations interest. note: really tested function linear models, generalized linear models, mixed model equivalents. mixed models, approach offer support incorporation random effects random slopes. just fixed effects, typically people want anyway. Users want better incorporate random intercepts slope find support merTools package.","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/get_sims.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Simulations from a Model Object (with New Data) — get_sims","text":"","code":"if (FALSE) { # \\dontrun{ # Note: these models are dumb, but they illustrate how it works. M1 <- lm(mpg ~ hp, mtcars) # Note: this function requires the DV to appear somewhere, anywhere in the \"new data\" newdat <- data.frame(mpg = 0, hp = c(mean(mtcars$hp) - sd(mtcars$hp), mean(mtcars$hp), mean(mtcars$hp) + sd(mtcars$hp))) get_sims(M1, newdat, 100, 8675309) # Note: this is likely a dumb model, but illustrates how it works. mtcars$mpgd <- ifelse(mtcars$mpg > 25, 1, 0) M2 <- glm(mpgd ~ hp, mtcars, family=binomial(link=\"logit\")) # Again: this function requires the DV to be somewhere, anywhere in the \"new data\" newdat$mpgd <- 0 # Note: the simulations are returned on their original \"link\". Here, that's a \"logit\" # You can adjust that accordingly. `plogis(y)` will convert those to probabilities. get_sims(M2, newdat, 100, 8675309) library(lme4) M3 <- lmer(mpg ~ hp + (1 | cyl), mtcars) # Random effects are not required here since we're passing over them. get_sims(M3, newdat, 100, 8675309) } # }"},{"path":"http://svmiller.com/reference/get_var_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a small data frame of the variable label and values. — get_var_info","title":"Get a small data frame of the variable label and values. — get_var_info","text":"get_var_info() allows peek labelled data, extracting given column's variable labels. intended use mostly \"peeking\" purpose recoding column's absence codebook form documentation. gvi() shortcut function.","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a small data frame of the variable label and values. — get_var_info","text":"","code":"get_var_info(.data, x) gvi(...)"},{"path":"http://svmiller.com/reference/get_var_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a small data frame of the variable label and values. — get_var_info","text":".data data frame x column within data frame ... optional, make shortcut (gvi) work","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a small data frame of the variable label and values. — get_var_info","text":"column data frame labelled, function returns message communicating absence labels. column data frame labelled, function returns small data frame communicating var_label() output (var), (often always) numeric \"code\" coinciding label (code), \"label\" attached (label).","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get a small data frame of the variable label and values. — get_var_info","text":"function leans var_label() val_label() labelled package, dependency package. function designed used \"pipe.\"","code":""},{"path":"http://svmiller.com/reference/get_var_info.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a small data frame of the variable label and values. — get_var_info","text":"","code":"library(tibble) library(dplyr) library(magrittr) ess9_labelled %>% get_var_info(netusoft) # works, as intended #> var code label #> 1 Internet use, how often 1 Never #> 2 Internet use, how often 2 Only occasionally #> 3 Internet use, how often 3 A few times a week #> 4 Internet use, how often 4 Most days #> 5 Internet use, how often 5 Every day #> 6 Internet use, how often 7 Refusal #> 7 Internet use, how often 8 Don't know #> 8 Internet use, how often 9 No answer ess9_labelled %>% get_var_info(cntry) # works, as intended #> var code label #> 1 Country GB United Kingdom #> 2 Country BE Belgium #> 3 Country DE Germany #> 4 Country EE Estonia #> 5 Country IE Ireland #> 6 Country BG Bulgaria #> 7 Country CH Switzerland #> 8 Country FI Finland #> 9 Country SI Slovenia #> 10 Country NL Netherlands #> 11 Country PL Poland #> 12 Country NO Norway #> 13 Country FR France #> 14 Country RS Serbia #> 15 Country AT Austria #> 16 Country IT Italy #> 17 Country HU Hungary #> 18 Country CY Cyprus #> 19 Country CZ Czechia ess9_labelled %>% get_var_info(ess9round) # barks at you; data are not labelled #> get_var_info() requires a labelled column. Otherwise, you get this message."},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":null,"dir":"Reference","previous_headings":"","what":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"theme_steve(), now stevethemes, preferred theme mine years ago. basically theme_bw() ggplot2 theme, tweaking things. moved theme_steve_web() things now, prominently website. theme incorporates \"Open Sans\" \"Titillium Web\" fonts like much. post_bg() legacy function changing backgrounds plots better match background color website. theme_steve_ms() LaTeX manuscripts use cochineal font package. theme_steve_font() purpose, allowing supply font.","code":""},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"","code":"theme_steve_web(...) post_bg(...) theme_steve_ms(axis_face = \"italic\", caption_face = \"italic\", ...) theme_steve_font(axis_face = \"italic\", caption_face = \"italic\", font, ...)"},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"... optional stuff, put anything . need . axis_face font face (\"plain\", \"italic\", \"bold\", \"bold.italic\"). Optional, defaults \"italic\". Applicable theme_steve_ms(). caption_face font face (\"plain\", \"italic\", \"bold\", \"bold.italic\"). Optional, defaults \"italic\". Applicable theme_steve_ms(). font font family plot. Applicable theme_steve_font().","code":""},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"post_bg() takes ggplot2 plot changes background color \"#fdfdfd\". theme_steve_web() extends theme_steve() add custom fonts, notably \"Open Sans\" \"Titillium Web\". cases, functions take ggplot2 plot return another ggplot2 plot, cosmetic changes. theme_steve_ms() takes ggplot2 plot overlays \"Crimson Pro\" fonts, basis cochineal font package LaTeX. theme_steve_font() takes ggplot2 plot overlays font choosing.","code":""},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"theme_steve_web() theme_steve_ms() explicitly depend fonts installed end. ultimately optional use functions imply . functions remain understood \"legacy\" functions longer maintained updated. stevethemes package ggplot2 elements going forward.","code":""},{"path":[]},{"path":"http://svmiller.com/reference/ggplot-themes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Legacy functions for Steve's Preferred ggplot2 Themes and Assorted Stuff — theme_steve_web","text":"","code":"if (FALSE) { # \\dontrun{ library(ggplot2) ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_web() + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice the prettier fonts, if you have them.\", caption = \"Data: ?mtcars in {datasets} in base R.\") ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_web() + post_bg() + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice the slight change in background color\", caption = \"Data: ?mtcars in {datasets} in base R.\") ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_ms() + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice the fonts will match the 'cochineal' font package in LaTeX.\", caption = \"Data: ?mtcars in {datasets} in base R.\") ggplot(mtcars, aes(x = mpg, y = hp)) + geom_point() + theme_steve_font(font = \"Comic Sans MS\") + labs(title = \"A ggplot2 Plot from the Motor Trend Car Road Tests Data\", subtitle = \"Notice that this will look ridiculous\", caption = \"Data: ?mtcars in {datasets} in base R.\") } # }"},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":null,"dir":"Reference","previous_headings":"","what":"German Dyad-Years, 1816-2020 — gmy_dyadyears","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"data generated peacesciencer German (Prussian) dyad-years 1816 2020. going useful stress-testing \"peace spell\" calculations may look like huge gap years. Correlates War context, Germany disappears international system 1945 1990. 'll also serve nice test making sure spell calculations misbehave context missing data. application, data disputes 2011 2020, dyad-years include 2011 2020.","code":""},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"","code":"gmy_dyadyears"},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"data frame 11174 observations following 6 variables. dyad unique identifier dyad ccode1 Correlates War state code Germany (255) ccode2 Correlates War state code state dyad year observation year dyad gmlmidongoing ongoing inter-state dispute dyad-year? gmlmidonset new inter-state dispute onset dyad-year","code":""},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"Data generated development version (scheduled release v. 0.7) peacesciencer. Conflict data come GML MID data (v. 2.2.1).","code":""},{"path":"http://svmiller.com/reference/gmy_dyadyears.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"German Dyad-Years, 1816-2020 — gmy_dyadyears","text":"Gibler, Douglas M., Steven V. Miller, Erin K. Little. 2016. “Analysis Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the Only Reproducible Seed That Matters — jenny","title":"Set the Only Reproducible Seed That Matters — jenny","text":"jenny() sets reproducible seed 8675309. reproducible seed use.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the Only Reproducible Seed That Matters — jenny","text":"","code":"jenny(x = 8675309)"},{"path":"http://svmiller.com/reference/jenny.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the Only Reproducible Seed That Matters — jenny","text":"x vector","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set the Only Reproducible Seed That Matters — jenny","text":"x specified 8675309, function sets reproducible seed 8675309 returns nice message congratulating . x 8675309, function sets reproducible seed gently admonishes wasting time.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the Only Reproducible Seed That Matters — jenny","text":"jenny() comes additional perks emo package installed. package optional.","code":""},{"path":"http://svmiller.com/reference/jenny.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set the Only Reproducible Seed That Matters — jenny","text":"","code":"jenny() # will work and reward you for it #> Jenny, I got your number... jenny(12345) # will not work and will result in a stern message #> Why are you using this function with some other reproducible seed..."},{"path":"http://svmiller.com/reference/linloess_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"linloess_plot() provides visual diagnostic linearity assumption OLS model. Provided linear model fit lm() base R, function extracts model frame creates faceted scatterplot. facet, linear smoother LOESS smoother estimated points. Users run function can assess just much linear smoother LOESS smoother diverge. diverge, user can determine much linear model good fit specified. plot also point potential outliers may need consideration.","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"","code":"linloess_plot( mod, resid = TRUE, smoother = \"loess\", se = TRUE, span = 0.75, suppress_warning = TRUE, ... ) # S3 method for class 'linloess' print(x, ...)"},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"mod fitted model, ideally simple linear model resid logical, defaults TRUE. FALSE, y-axis plots raw values dependent variable. TRUE, y-axis model's residuals. Either work well matter hand, provided treat output illustrative suggestive. smoother defaults \"loess\", passed 'method' argument non-linear smoother. se logical, defaults TRUE. TRUE, gives standard error estimates assorted smoothers. resid TRUE, standard error flat line 0. span numeric, defaults .75. adjustment smoother. Higher values permit smoother lines might warranted presence sparse pockets data. suppress_warning logical, defaults TRUE. TRUE, plot suppresses assorted warnings LOESS smoother otherwise cautioning things eyes otherwise see. ... Additional arguments context print function (used) x ggplot object special 'linloess' class","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"linloess_plot() returns faceted scatterplot ggplot2 object. linear smoother solid blue (blue standard error bands) LOESS smoother dashed black line (gray/default standard error bands). can add cosmetic features fact. function may spit warnings related LOESS smoother, depending data whether disabled warnings function. think fine extent really just visual aid informal diagnostic linearity assumption.","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"function makes implicit assumption variable regression formula name \".y\" \".resid\". may interest (sake rudimentary diagnostic checks) disable standard error bands particularly ill-fitting linear models.","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/linloess_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compare Linear Smoother to LOESS Smoother for Your Linear Model — linloess_plot","text":"","code":"M1 <- lm(mpg ~ am + carb + disp, data=mtcars) linloess_plot(M1) #> `geom_smooth()` using formula = 'y ~ x' #> `geom_smooth()` using formula = 'y ~ x' linloess_plot(M1, color=\"black\", pch=21) #> `geom_smooth()` using formula = 'y ~ x' #> `geom_smooth()` using formula = 'y ~ x'"},{"path":"http://svmiller.com/reference/make_perclab.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"make_perclab() takes proportion, multiplies 100, optionally rounds , pastes percentage sign next .","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"","code":"make_perclab(x, d = 2)"},{"path":"http://svmiller.com/reference/make_perclab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"x numeric vector d digits round. Defaults 2.","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"function takes proportion, multiplies 100, (optionally) rounds set decimal point, pastes percentage sign next .","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"function useful modeling proportions something like bar chart (proportions flexible) want label bar percentage. function mostly cosmetic.","code":""},{"path":"http://svmiller.com/reference/make_perclab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make Percentage Label for Proportion and Add Percentage Sign — make_perclab","text":"","code":"x <- runif(100) make_perclab(x) #> [1] \"15.95%\" \"47.82%\" \"76.48%\" \"76.97%\" \"26.85%\" \"67.3%\" \"97.88%\" \"84.63%\" #> [9] \"85.67%\" \"44.52%\" \"83.82%\" \"58.33%\" \"51.1%\" \"26.02%\" \"74.95%\" \"91.82%\" #> [17] \"71.64%\" \"20.62%\" \"81.69%\" \"71.59%\" \"6.06%\" \"84.71%\" \"84.68%\" \"33.26%\" #> [25] \"55.97%\" \"66.95%\" \"25.46%\" \"7.92%\" \"16%\" \"81.64%\" \"97.57%\" \"84.21%\" #> [33] \"32.93%\" \"59.26%\" \"18.39%\" \"45.15%\" \"44.02%\" \"35.81%\" \"20.39%\" \"72.15%\" #> [41] \"97.65%\" \"94.42%\" \"51.75%\" \"84.29%\" \"34.3%\" \"3.89%\" \"31.81%\" \"13.59%\" #> [49] \"33.91%\" \"76.23%\" \"75.28%\" \"43.36%\" \"75.5%\" \"40.68%\" \"70.32%\" \"31.16%\" #> [57] \"42.62%\" \"22.76%\" \"64.91%\" \"14.96%\" \"64.66%\" \"27.46%\" \"87.57%\" \"79.56%\" #> [65] \"94.24%\" \"78.67%\" \"72.19%\" \"86.53%\" \"26.93%\" \"0.19%\" \"40.95%\" \"74.39%\" #> [73] \"52.55%\" \"86.38%\" \"37.7%\" \"82.4%\" \"40.19%\" \"37.85%\" \"43.89%\" \"43.71%\" #> [81] \"26.26%\" \"44.05%\" \"49.85%\" \"85.97%\" \"51.82%\" \"11.51%\" \"75.3%\" \"16.19%\" #> [89] \"40.05%\" \"18.23%\" \"44%\" \"74.73%\" \"19.08%\" \"95.5%\" \"2.4%\" \"23.09%\" #> [97] \"59.39%\" \"23.55%\" \"51.44%\" \"64.3%\""},{"path":"http://svmiller.com/reference/make_scale.html","id":null,"dir":"Reference","previous_headings":"","what":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"make_scale() rescale vector user-defined minimum maximum.","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"","code":"make_scale(x, minim, maxim)"},{"path":"http://svmiller.com/reference/make_scale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"x numeric vector minim desired numeric minimum maxim desired numeric maximum","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"function takes numeric vector returns rescaled version observed (desired) minimum, observed (desired) maximum, rescaled values extremes.","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"function useful wanted kind minimum-maximum rescaling variable given scale, prominently rescaling minimum 0 maximum 1 (thinking ahead regression). function flexible enough minimum maximum.","code":""},{"path":"http://svmiller.com/reference/make_scale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Rescale Vector to Arbitrary Minimum and Maximum — make_scale","text":"","code":"x <- runif(100, 1, 100) make_scale(x, 2, 5) # works #> [1] 3.691435 2.107502 3.670319 2.808087 2.615673 2.354313 2.035948 3.128916 #> [9] 3.787428 2.048508 2.514866 3.244003 2.778355 4.685685 4.348520 3.125769 #> [17] 4.614064 3.065012 3.651240 4.612916 4.896847 2.237435 4.184424 3.979520 #> [25] 3.335907 2.936188 2.354956 2.975470 3.351972 4.891430 4.500238 3.191101 #> [33] 3.957162 4.667745 2.537577 2.117305 2.272307 4.773745 3.803732 4.907440 #> [41] 3.106993 2.655200 4.768845 3.331101 3.252278 4.399584 2.000000 2.862249 #> [49] 2.534993 4.341187 4.848127 2.970642 2.520859 2.757290 2.593809 3.852103 #> [57] 4.917708 2.434919 2.254518 2.785304 3.631706 2.729471 4.742084 4.935707 #> [65] 4.232052 3.276069 3.551173 3.827683 4.342697 4.850768 2.397893 2.485624 #> [73] 4.386657 4.911031 4.597821 2.292243 3.615681 4.947668 2.276199 2.769858 #> [81] 3.763996 4.694063 4.213002 4.505863 2.531546 4.016657 2.649579 3.645823 #> [89] 4.208062 2.575016 4.158535 4.681841 4.639911 3.903428 3.686708 2.344965 #> [97] 3.054974 3.248985 5.000000 4.889466 make_scale(x, 5, 2) # results in message #> The desired minimum should not be greater than or equal to the desired maximum. Try again. make_scale(x, 0, 1) # probably why you're using this. #> [1] 0.56381179 0.03583408 0.55677310 0.26936218 0.20522434 0.11810428 #> [7] 0.01198278 0.37630528 0.59580925 0.01616923 0.17162203 0.41466759 #> [13] 0.25945173 0.89522844 0.78283997 0.37525631 0.87135473 0.35500402 #> [19] 0.55041347 0.87097216 0.96561569 0.07914503 0.72814125 0.65983986 #> [25] 0.44530222 0.31206271 0.11831883 0.32515659 0.45065717 0.96380997 #> [31] 0.83341280 0.39703383 0.65238741 0.88924841 0.17919234 0.03910175 #> [37] 0.09076887 0.92458181 0.60124387 0.96914665 0.36899770 0.21839989 #> [43] 0.92294842 0.44370037 0.41742612 0.79986144 0.00000000 0.28741634 #> [49] 0.17833088 0.78039566 0.94937583 0.32354748 0.17361974 0.25243013 #> [55] 0.19793622 0.61736765 0.97256942 0.14497303 0.08483947 0.26176803 #> [61] 0.54390194 0.24315704 0.91402786 0.97856894 0.74401739 0.42535642 #> [67] 0.51705782 0.60922758 0.78089907 0.95025615 0.13263085 0.16187452 #> [73] 0.79555246 0.97034368 0.86594029 0.09741447 0.53856031 0.98255591 #> [79] 0.09206622 0.25661945 0.58799873 0.89802115 0.73766743 0.83528771 #> [85] 0.17718193 0.67221891 0.21652650 0.54860782 0.73602058 0.19167198 #> [91] 0.71951174 0.89394686 0.87997035 0.63447593 0.56223588 0.11498833 #> [97] 0.35165797 0.41632849 1.00000000 0.96315523"},{"path":"http://svmiller.com/reference/map_quiz.html","id":null,"dir":"Reference","previous_headings":"","what":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"simple data set records every wrong guess map quiz assignments gave intro IR class Clemson University across five semesters.","code":""},{"path":"http://svmiller.com/reference/map_quiz.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"","code":"map_quiz"},{"path":"http://svmiller.com/reference/map_quiz.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"data frame 1772 observations following 8 variables. class ordered factor semester wrong guess recorded student. Levels include \"Spring 2018\", \"Fall 2018\", \"Spring 2019\", \"Fall 2019\", \"Spring 2020.\" students number students class taking map quiz. region region map country located. Values include \"Europe\", \"Africa\", \"Asia\", \"Latin America\", \"MENA.\" \"MENA\" short \"Middle East North Africa.\" country country asked student correctly identify guess country actual state incorrectly guessed student ccode1 Correlates War state code state wanted student identify country. ccode2 Correlates War state code state wrong guess state guess mindist minimum distance (kilometers) country guess","code":""},{"path":"http://svmiller.com/reference/map_quiz.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Map Quiz Wrong Guesses Across Five Intro to IR Courses — map_quiz","text":"Students can always make guess wrong, explains NAs data. Students given five separate numbered maps prompted identify 10 countries . maps never changed across five semesters, prompts. Use data see fit. Obviously, FERPA considerations mean share anything else potential value .","code":""},{"path":"http://svmiller.com/reference/mround.html","id":null,"dir":"Reference","previous_headings":"","what":"Multiply a Number by 100 and Round It (By Default: 2) — mround","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"mround() convenience function wrote annotating bar charts make. Assuming proportion variable, mround() multiply value 100 round presentation. default, rounds two. user can adjust .","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"","code":"mround(x, d = 2)"},{"path":"http://svmiller.com/reference/mround.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"x numeric vector d number decimal points user wants round. set, rounds two decimal points.","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"function takes numeric vector, multiplies 100, rounds (two digits default), returns user.","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"sister function make_perclab() package. , however, add percentage sign.","code":""},{"path":"http://svmiller.com/reference/mround.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Multiply a Number by 100 and Round It (By Default: 2) — mround","text":"","code":"x <- runif(100) mround(x) #> [1] 77.17 86.70 8.91 30.97 62.94 93.54 49.49 18.95 32.14 32.52 58.12 84.82 #> [13] 67.87 52.93 22.46 68.72 14.33 24.87 39.63 61.16 4.70 34.00 60.88 5.18 #> [25] 29.85 34.66 87.61 41.54 76.29 22.50 66.38 40.79 41.30 67.60 95.44 27.72 #> [37] 45.55 16.41 15.38 7.23 93.26 31.99 24.51 53.31 91.62 22.81 12.14 34.06 #> [49] 61.89 85.55 65.54 60.79 49.81 65.56 88.59 26.24 72.44 22.17 31.03 15.95 #> [61] 74.47 83.76 77.94 75.31 23.87 59.91 24.03 64.50 80.73 80.78 31.72 34.27 #> [73] 47.95 3.81 51.50 3.64 47.21 2.44 31.31 72.60 24.25 95.20 4.47 57.24 #> [85] 83.92 96.72 16.90 25.97 30.70 73.46 43.82 85.82 87.88 26.51 70.31 30.54 #> [97] 90.63 6.73 60.41 15.60 mround(x, 2) # same as above #> [1] 77.17 86.70 8.91 30.97 62.94 93.54 49.49 18.95 32.14 32.52 58.12 84.82 #> [13] 67.87 52.93 22.46 68.72 14.33 24.87 39.63 61.16 4.70 34.00 60.88 5.18 #> [25] 29.85 34.66 87.61 41.54 76.29 22.50 66.38 40.79 41.30 67.60 95.44 27.72 #> [37] 45.55 16.41 15.38 7.23 93.26 31.99 24.51 53.31 91.62 22.81 12.14 34.06 #> [49] 61.89 85.55 65.54 60.79 49.81 65.56 88.59 26.24 72.44 22.17 31.03 15.95 #> [61] 74.47 83.76 77.94 75.31 23.87 59.91 24.03 64.50 80.73 80.78 31.72 34.27 #> [73] 47.95 3.81 51.50 3.64 47.21 2.44 31.31 72.60 24.25 95.20 4.47 57.24 #> [85] 83.92 96.72 16.90 25.97 30.70 73.46 43.82 85.82 87.88 26.51 70.31 30.54 #> [97] 90.63 6.73 60.41 15.60 mround(x, 3) #> [1] 77.172 86.698 8.905 30.967 62.935 93.542 49.492 18.947 32.144 32.524 #> [11] 58.119 84.819 67.867 52.925 22.461 68.724 14.332 24.873 39.633 61.155 #> [21] 4.699 33.996 60.877 5.179 29.851 34.662 87.609 41.538 76.293 22.500 #> [31] 66.381 40.790 41.301 67.601 95.436 27.716 45.554 16.414 15.384 7.225 #> [41] 93.260 31.993 24.513 53.315 91.617 22.808 12.136 34.064 61.887 85.549 #> [51] 65.538 60.786 49.807 65.555 88.587 26.241 72.441 22.170 31.025 15.948 #> [61] 74.472 83.756 77.942 75.306 23.872 59.912 24.030 64.500 80.728 80.779 #> [71] 31.717 34.270 47.949 3.805 51.502 3.644 47.212 2.442 31.313 72.599 #> [81] 24.251 95.197 4.468 57.236 83.924 96.723 16.904 25.972 30.699 73.460 #> [91] 43.823 85.825 87.883 26.506 70.314 30.537 90.630 6.727 60.409 15.596"},{"path":"http://svmiller.com/reference/nin.html","id":null,"dir":"Reference","previous_headings":"","what":"Find Non-Matching Elements — %nin%","title":"Find Non-Matching Elements — %nin%","text":"%nin% finds non-matching elements given vector. negation %%.","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find Non-Matching Elements — %nin%","text":"","code":"a %nin% b"},{"path":"http://svmiller.com/reference/nin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find Non-Matching Elements — %nin%","text":"vector (character, factor, numeric) b vector (character, factor, numeric)","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find Non-Matching Elements — %nin%","text":"%nin% finds non-matching elements returns one two things, depending use. two simple vectors, report matches . comparing vector within data frame, effect reporting rows data frame match supplied (second) vector.","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Find Non-Matching Elements — %nin%","text":"simple negation %%. use mostly columns data frame.","code":""},{"path":"http://svmiller.com/reference/nin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find Non-Matching Elements — %nin%","text":"","code":"library(tibble) library(dplyr) # Watch this subset stuff dat <- tibble(x = seq(1:10), d = rnorm(10)) filter(dat, x %nin% c(3, 6, 9)) #> # A tibble: 7 × 2 #> x d #> #> 1 1 -0.319 #> 2 2 0.915 #> 3 4 -1.10 #> 4 5 -0.605 #> 5 7 -2.09 #> 6 8 -0.934 #> 7 10 -0.398"},{"path":"http://svmiller.com/reference/normal_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"Make and annotate a normal distribution with ggplot2 — normal_dist","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"normal_dist() convenience function making plot normal distribution annotated areas underneath normal curve.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"","code":"normal_dist(curvecolor, fillcolor, fontfamily)"},{"path":"http://svmiller.com/reference/normal_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"curvecolor color curve . ggplot2-recognized format . fillcolor color area underneath curve . ggplot2-recognized format . fontfamily Font family labeling areas underneath curve. OPTIONAL. can omit like.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"function returns fancy plot normal distribution annotated areas underneath hood. Note whatever color supplied fillcolor automatically lightened areas center curve.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"normal distribution standard normal distribution mean 0 standard deviation 1.","code":""},{"path":"http://svmiller.com/reference/normal_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make and annotate a normal distribution with ggplot2 — normal_dist","text":"","code":"library(stevemisc) normal_dist(\"blue\",\"red\") normal_dist(\"purple\",\"orange\")"},{"path":"http://svmiller.com/reference/p_z.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert the p-value you want to the z-value it actually is — p_z","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"loathe statistical instruction privileges obtaining magical p-value reference area underneath standard normal curve, botch actual z-value corresponding magical p-value. simple function converts p-value want (typically .05, thanks R.. Fisher) z-value actually kind claims typically make inferential statistics. going inference wrong way, least get z-value right.","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"","code":"p_z(x, ts = TRUE)"},{"path":"http://svmiller.com/reference/p_z.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"x numeric vector (one multiple) 0 1 ts logical, defaults TRUE. TRUE, returns two-sided critical z-value. FALSE, function returns one-sided critical z-value.","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"function takes numeric vector, corresponding p-value want, returns numeric vector coinciding z-value want standard normal distribution. example, z-value corresponding magic number .05 (conventional cutoff assessing statistical significance) 1.96, something like 1.959964 (rounding default six decimal points).","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"p_z() takes p-value interest converts , precision, z-value actually . function takes vector returns vector. function assumes something akin calculating confidence interval testing regression coefficient null hypothesis zero. means default output two-sided critical z-value. taught use two-sided z-values agnostic direction effect statistic interest, , frank, hilarious given research typically done.","code":""},{"path":"http://svmiller.com/reference/p_z.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert the p-value you want to the z-value it actually is — p_z","text":"","code":"library(stevemisc) p_z(.05) #> [1] 1.959964 p_z(c(.001, .01, .05, .1)) #> [1] 3.290527 2.575829 1.959964 1.644854 p_z(.05, ts=FALSE) #> [1] 1.644854 p_z(c(.001, .01, .05, .1), ts=FALSE) #> [1] 3.090232 2.326348 1.644854 1.281552"},{"path":"http://svmiller.com/reference/prepare_refs.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"prepare_refs last-minute formatting data frame created bib2df can formatted nicely various outputs.","code":""},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"","code":"prepare_refs(bib2df_refs, toformat = \"plain\")"},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"bib2df_refs data frame created bib2df toformat type output ultimately going want print_refs() . Default \"plain\".","code":""},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"print_refs() last-minute formatting data frame created bib2df rendering R Markdown little easier less code-heavy.","code":""},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"function designed work generally absence various fields. Assume, example, data frame BOOK field. function uses one_of() wrapper work around . \"warning\" returned function message. function may expanded think use cases.","code":""},{"path":[]},{"path":"http://svmiller.com/reference/prepare_refs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Prepare bib2df Data Frame for Formatting to Various Outputs — prepare_refs","text":"","code":"prepare_refs(stevepubs) #> Warning: Unknown columns: `BOOK` #> Warning: Unknown columns: `MAINTITLE` #> # A tibble: 19 × 12 #> CATEGORY BIBTEXKEY AUTHOR BOOKTITLE JOURNAL NUMBER PAGES PUBLISHER TITLE #> #> 1 ARTICLE millergib… NA *Confl… 3 261-… NA \"Dem… #> 2 ARTICLE giblereta… NA *Compa… 12 1655… NA \"Ind… #> 3 ARTICLE giblermil… NA *Socia… 5 1202… NA \"Com… #> 4 ARTICLE giblermil… NA *Journ… 2 258-… NA \"Qui… #> 5 ARTICLE miller201… NA *Journ… 6 677-… NA \"Ter… #> 6 ARTICLE giblermil… NA *Journ… 5 634-… NA \"Ext… #> 7 ARTICLE giblereta… NA *Inter… 4 719-… NA \"An … #> 8 ARTICLE miller201… NA *Polit… 2 457-… NA \"Eco… #> 9 ARTICLE miller201… NA *Confl… 5 526-… NA \"Ind… #> 10 ARTICLE miller201… NA *Polit… 4 790-… NA \"The… #> 11 ARTICLE miller201… NA *Peace… 1 NA NA \"Ext… #> 12 ARTICLE miller201… NA *Socia… 1 272-… NA \"Wha… #> 13 ARTICLE giblereta… NA *Inter… 2 476-… NA \"The… #> 14 INCOLLECTION millereta… *Oxford … NA NA NA Oxford U… \"Geo… #> 15 ARTICLE millerdav… NA *Journ… 2 334-… NA \"The… #> 16 ARTICLE curtismil… NA *Europ… 2 202-… NA \"A (… #> 17 ARTICLE miller202… NA *The S… NA NA NA \"Eco… #> 18 ARTICLE peacescie… NA *Confl… NA NA NA \"~{ … #> 19 ARTICLE miller202… NA *Journ… NA NA NA \"A R… #> # ℹ 3 more variables: VOLUME , YEAR , DOI "},{"path":"http://svmiller.com/reference/print_refs.html","id":null,"dir":"Reference","previous_headings":"","what":"Print and Format .bib Entries as References — print_refs","title":"Print and Format .bib Entries as References — print_refs","text":"print_refs() convenience function found edited allow user print format .bib entries references. function useful want load .bib entry set entries print middle document R Markdown.","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print and Format .bib Entries as References — print_refs","text":"","code":"print_refs( bib, csl = \"american-political-science-association.csl\", toformat = \"markdown_strict\", cslrepo = \"https://raw.githubusercontent.com/citation-style-language/styles/master\", spit_out = TRUE, delete_after = TRUE )"},{"path":"http://svmiller.com/reference/print_refs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print and Format .bib Entries as References — print_refs","text":"bib valid .bib entry csl CSL file, matching one available Github repository, user wants format references. Default \"american-political-science-association.csl\". toformat output wanted user. Default \"markdown_strict\". cslrepo directory CSL files. Defaults one Github. spit_out logical, defaults TRUE. TRUE, wraps (\"spits \") formatted citations writeLines() output console. FALSE, returns character vector. delete_after logical, defaults TRUE. TRUE, deletes CSL file done. FALSE, retains CSL (potential) future use.","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Print and Format .bib Entries as References — print_refs","text":"print_refs() takes .bib entry returns requested formatted reference references .","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Print and Format .bib Entries as References — print_refs","text":"print_refs() assumes active internet connection absence appropriate CSL file working directory. citation style language (CSL) file supplied user must match file massive Github repository CSL files. Users interested potential outputs read Pandoc (https://pandoc.org/MANUAL.html). Github repository CSL files available : https://github.com/citation-style-language/styles.","code":""},{"path":"http://svmiller.com/reference/print_refs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Print and Format .bib Entries as References — print_refs","text":"","code":"# \\donttest{ example <- \"@Book{vasquez2009twp, Title = {The War Puzzle Revisited}, Author = {Vasquez, John A}, Publisher = {New York, NY: Cambridge University Press}, Year = {2009}}\" print_refs(example) #> I'm going to assume this is a .bib entry... #> Downloading CSL from https://raw.githubusercontent.com/citation-style-language/styles/master/american-political-science-association.csl #> Vasquez, John A. 2009. *The War Puzzle Revisited*. New York, NY: #> Cambridge University Press. # }"},{"path":"http://svmiller.com/reference/ps_btscs.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ","title":"Create ","text":"ps_btscs() allows create spells (\"peace years\" international conflict context) observations event. allow researcher better model temporal dependence binary time-series cross-section (\"BTSCS\") models. improvement sbtscs() (included package) ability flexibly work data lots NAs bracket observed event data. used peacesciencer package.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ","text":"","code":"ps_btscs(data, event, tvar, csunit, pad_ts = FALSE)"},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ","text":"data data set working event event (0, 1) want spells peace years tvar time variable (e.g. year) csunit cross-sectional unit (likely dyad boilerplate international conflict stuff) pad_ts time-series filled panels unbalanced/gaps? Defaults FALSE.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ","text":"ps_btscs() takes data frame returns data frame new variable named spell.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ","text":"function derived sbtscs(). See documentation information.","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create ","text":"Armstrong, Dave. 2016. “DAMisc: Dave Armstrong's Miscellaneous Functions.” R package version 1.4-3. Miller, Steven V. 2017. “Quickly Create Peace Years BTSCS Models sbtscs stevemisc.” http://svmiller.com/blog/2017/06/quickly-create-peace-years--btscs-models--stevemisc/","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create ","text":"David . Armstrong, Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/ps_btscs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ","text":"","code":"# \\donttest{ library(dplyr) library(stevemisc) data(usa_mids) # notice: no quotes ps_btscs(usa_mids, midongoing, year, dyad) #> Joining with `by = join_by(dyad, year)` #> # A tibble: 14,586 × 7 #> dyad ccode1 ccode2 year midongoing midonset spell #> #> 1 1002020 2 20 1920 0 0 0 #> 2 1002020 2 20 1921 0 0 1 #> 3 1002020 2 20 1922 0 0 2 #> 4 1002020 2 20 1923 0 0 3 #> 5 1002020 2 20 1924 0 0 4 #> 6 1002020 2 20 1925 0 0 5 #> 7 1002020 2 20 1926 0 0 6 #> 8 1002020 2 20 1927 0 0 7 #> 9 1002020 2 20 1928 0 0 8 #> 10 1002020 2 20 1929 0 0 9 #> # ℹ 14,576 more rows # }"},{"path":"http://svmiller.com/reference/ps_spells.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ","title":"Create ","text":"ps_spells() allows create spells (\"peace years\" international conflict context) observations event. allow researcher better model temporal dependence binary time-series cross-section (\"BTSCS\") models. function one three package, contents function partly ported add_duration() function spduration package. function, unlike two offer , works much better panels decidedly imbalanced.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ","text":"","code":"ps_spells(data, event, tvar, csunit, time_type = \"year\", ongoing = FALSE)"},{"path":"http://svmiller.com/reference/ps_spells.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ","text":"data data set working event event (0, 1) want spells tvar time variable (e.g. year) csunit cross-sectional unit (e.g. dyad leader) time_type type time-unit data? Right now, work years support months days forthcoming. anything argument just yet. ongoing TRUE, successive 1s considered ongoing events treated NA first 1. FALSE, successive 1s treated failures. Defaults FALSE.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ","text":"ps_spells() takes data frame returns data frame new variable named spell.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ","text":"function derived add_duration() spduration package. See documentation information. thank Andreas Beger blessing port parts .","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create ","text":"Beger, Andreas, Daina Chiba, Daniel W. Hill, Jr, Nils W. Metternich, Shahryar Minhas Michael D. Ward. 2018. “spduration: Split-Population Duration (Cure) Regression.” R package version 0.17.1.","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create ","text":"Andreas Beger, Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/ps_spells.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ","text":"","code":"One <- ps_btscs(usa_mids, midongoing, year, dyad) #> Joining with `by = join_by(dyad, year)` Two <- ps_spells(usa_mids, midongoing, year, dyad) #> Joining with `by = join_by(orig_order)` identical(One, Two) #> [1] TRUE"},{"path":"http://svmiller.com/reference/r1sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale a vector by one standard deviation — r1sd","title":"Scale a vector by one standard deviation — r1sd","text":"r1sd allows rescale numeric vector ensuing output mean 0 standard deviation 1.","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale a vector by one standard deviation — r1sd","text":"","code":"r1sd(x, na = TRUE)"},{"path":"http://svmiller.com/reference/r1sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scale a vector by one standard deviation — r1sd","text":"x numeric vector na NAs vector. Defaults TRUE (.e. passes missing observations)","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scale a vector by one standard deviation — r1sd","text":"function returns numeric vector rescaled mean 0 standard deviation 1.","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale a vector by one standard deviation — r1sd","text":"convenience function since default rescale() function additional weirdness welcome use cases. default, na.rm set TRUE.","code":""},{"path":"http://svmiller.com/reference/r1sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale a vector by one standard deviation — r1sd","text":"","code":"x <- rnorm(100) r1sd(x) #> [1] 0.8589857352 0.6882792081 -0.3820161132 -0.2185607108 -2.3581866933 #> [6] -1.1186076412 -0.9259636957 0.3993068214 -0.9705208235 0.9448918531 #> [11] -1.9134339921 0.5798526209 0.6713547170 0.2153457412 1.3869096436 #> [16] 0.3956947758 -0.4787230015 0.4141902533 -0.8131107521 -0.9124984890 #> [21] 1.7734287396 1.3392256447 0.1729591211 -1.4489497087 -0.0413043116 #> [26] 0.0220676683 -2.2579591760 -0.7365099747 -1.7214374886 0.9269118133 #> [31] 0.2402823685 -1.5983065925 0.1540897909 0.4292600416 0.5623382525 #> [36] 0.9417246305 0.7862868975 0.4378974028 0.5348330059 1.3801393606 #> [41] -0.0601846493 0.0949865211 1.2914664780 -0.0007232612 0.9247860528 #> [46] -0.1117526441 -0.5317138969 -0.1530381957 0.8195438764 -1.0799893600 #> [51] 0.2095907871 -0.2415049796 2.6475450451 -1.0827106024 -0.3501653160 #> [56] 0.2515200939 1.9588504804 -0.2365637631 0.6123825135 -1.9958963302 #> [61] 0.3751642922 0.9637732620 0.4935186982 0.1102981212 -0.0458281813 #> [66] 0.0081144400 -2.4718601906 0.3977882087 1.1481818390 1.4765321954 #> [71] -0.4463775814 1.2445406548 -0.7944924161 -1.4013223523 1.2760452552 #> [76] -0.5366643254 0.0709421095 -1.0050927198 1.3207408527 1.0405286073 #> [81] 0.4652549313 -0.0473292065 -0.6480720990 0.9625997253 -1.4998238910 #> [86] -0.8196351690 -1.2349042335 0.8690667709 -0.6636137487 -0.9215064628 #> [91] -0.2838803156 -0.1382895314 -0.1508800263 1.0792471933 -0.8381494592 #> [96] 0.5004442955 -0.0965475607 -1.6511651186 0.5083293670 0.0577279770"},{"path":"http://svmiller.com/reference/r2sd.html","id":null,"dir":"Reference","previous_headings":"","what":"Scale a vector (or vectors) by two standard deviations — r2sd","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"r2sd allows rescale numeric vector ensuing output mean 0 standard deviation .5. r2sd_at wrapper mutate_at rename_at dplyr. rescales supplied vectors new vectors renames vectors prefix z_.","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"","code":"r2sd(x, na = TRUE)"},{"path":"http://svmiller.com/reference/r2sd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"x vector, likely data frame na NAs vector. Defaults TRUE (.e. passes missing observations)","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"function returns numeric vector rescaled mean 0 standard deviation .5.","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"default, na.rm set TRUE. missing data, function just pass . Gelman (2008) argues rescaling two standard deviations puts regression inputs roughly scale matter original scale. allows honest, preliminary, assessment relative effect sizes regression output. , without requiring rescale function arm. trying reduce packages workflow relies. Importantly, tend rescale ordinal interval inputs leave binary inputs 0/1. , r2sd function fancier -else statements Gelman's rescale function .","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"Gelman, Andrew. 2008. \"Scaling Regression Inputs Dividing Two Standard Deviations.\" Statistics Medicine 27: 2865–2873.","code":""},{"path":"http://svmiller.com/reference/r2sd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Scale a vector (or vectors) by two standard deviations — r2sd","text":"","code":"x <- rnorm(100) r2sd(x) #> [1] 0.58938435 0.18197583 0.58039130 0.91074730 0.01668217 -0.18478770 #> [7] -0.01962033 -0.42179684 0.36908293 0.06542483 -0.88627356 0.28892837 #> [13] -0.98614536 0.50553860 -0.51143024 -0.23288148 0.31638425 -0.16144326 #> [19] -0.07254027 1.07923937 1.02523837 0.21173516 0.01595284 0.21346861 #> [25] -0.36107322 -0.05857578 -1.27614690 -0.35787795 -0.22397223 0.15244818 #> [31] -0.23264380 0.95027701 -0.03677793 -0.54346030 0.43066237 -0.39212414 #> [37] -0.58999446 0.58785329 -0.08272961 -0.05357957 0.17797965 0.60034952 #> [43] -0.25598906 0.04699418 0.15419367 0.58142698 0.74035987 0.56813722 #> [49] -0.09961741 0.03155173 0.36507027 0.24989286 0.13771068 -0.08150362 #> [55] 0.40591857 -0.22073909 1.06251295 -0.14558226 -0.53391304 -0.61590666 #> [61] 0.03739685 0.00918478 -0.24785673 -0.34481666 0.39803186 -1.28439892 #> [67] 0.42355915 -0.14919976 0.53538693 0.43795067 0.25980651 0.23653250 #> [73] -0.31081525 -0.53525793 -0.02476267 0.27148311 -1.27299036 -0.55505474 #> [79] -0.67254477 -0.12399040 -1.14078240 -0.22691123 -0.64409311 0.43743081 #> [85] 0.15814365 -0.09573691 -0.19889374 -0.21807596 0.06221853 0.08736401 #> [91] 0.59476649 -0.18209050 0.70475613 0.33387888 0.25281224 -0.22382176 #> [97] 0.29469543 0.21196985 -0.64322852 -0.62643329 r2sd_at(mtcars, c(\"mpg\", \"hp\", \"disp\")) #> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> z_mpg z_hp z_disp #> Mazda RX4 0.07544241 -0.26754642 -0.28530991 #> Mazda RX4 Wag 0.07544241 -0.26754642 -0.28530991 #> Datsun 710 0.22477172 -0.39152023 -0.49509105 #> Hornet 4 Drive 0.10862670 -0.26754642 0.11004685 #> Hornet Sportabout -0.11536726 0.20647109 0.52154061 #> Valiant -0.16514370 -0.30400931 -0.02308349 #> Duster 360 -0.48039447 0.71695148 0.52154061 #> Merc 240D 0.35750889 -0.61759012 -0.33896547 #> Merc 230 0.22477172 -0.37693508 -0.36276756 #> Merc 280 -0.07388690 -0.17274292 -0.25464959 #> Merc 280C -0.19003192 -0.17274292 -0.25464959 #> Merc 450SE -0.30617694 0.24293397 0.18185654 #> Merc 450SL -0.23151228 0.24293397 0.18185654 #> Merc 450SLC -0.40572981 0.24293397 0.18185654 #> Cadillac Fleetwood -0.80394131 0.42524840 0.97337691 #> Lincoln Continental -0.80394131 0.49817417 0.92496588 #> Chrysler Imperial -0.44721018 0.60756282 0.84428082 #> Fiat 128 1.02119472 -0.58841981 -0.61329465 #> Honda Civic 0.85527326 -0.69051589 -0.62539740 #> Toyota Corolla 1.14563581 -0.59571239 -0.64395497 #> Toyota Corona 0.11692278 -0.36234992 -0.44627659 #> Dodge Challenger -0.38084159 0.02415666 0.35210200 #> AMC Javelin -0.40572981 0.02415666 0.29562247 #> Camaro Z28 -0.56335520 0.71695148 0.48119809 #> Pontiac Firebird -0.07388690 0.20647109 0.68291072 #> Fiat X1-9 0.59809500 -0.58841981 -0.61208437 #> Porsche 914-2 0.49024605 -0.40610538 -0.44546974 #> Lotus Europa 0.85527326 -0.24566869 -0.54713290 #> Ford Pantera L -0.35595337 0.85551044 0.48523234 #> Ferrari Dino -0.03240653 0.20647109 -0.34582370 #> Maserati Bora -0.42232196 1.37328341 0.28351971 #> Volvo 142E 0.10862670 -0.27483900 -0.44264576"},{"path":"http://svmiller.com/reference/rbnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"rbnorm() function randomly generate values bounded normal (really: scaled beta) distribution specified mean, standard deviation, upper/lower bounds. use function randomly generate data treat interval sake getting means standard deviations, discernible bounds (even skew) teach students things like random sampling central limit theorem.","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"","code":"rbnorm(n, mean, sd, lowerbound, upperbound, round = FALSE, seed)"},{"path":"http://svmiller.com/reference/rbnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"n number observations simulate mean mean approximate sd standard deviation approximate lowerbound lower bound data generated upperbound upper bound data generated round whether round values whole integers. Defaults FALSE seed set optional seed","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"function returns vector simulated data approximating user-specified conditions.","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"call \"bounded normal\" really beta distribution. aware . took much code somewhere. forget .","code":""},{"path":"http://svmiller.com/reference/rbnorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bounded Normal (Really: Scaled Beta) Distribution — rbnorm","text":"","code":"library(tibble) tibble(x = rbnorm(10000, 57, 14, 0, 100)) #> # A tibble: 10,000 × 1 #> x #> #> 1 51.3 #> 2 75.9 #> 3 57.1 #> 4 66.4 #> 5 34.2 #> 6 71.4 #> 7 29.8 #> 8 42.5 #> 9 66.2 #> 10 56.2 #> # ℹ 9,990 more rows tibble(x = rbnorm(10000, 57, 14, 0, 100, round = TRUE)) #> # A tibble: 10,000 × 1 #> x #> #> 1 58 #> 2 27 #> 3 56 #> 4 53 #> 5 49 #> 6 80 #> 7 47 #> 8 74 #> 9 61 #> 10 76 #> # ℹ 9,990 more rows tibble(x = rbnorm(10000, 57, 14, 0, 100, seed = 8675309)) #> # A tibble: 10,000 × 1 #> x #> #> 1 72.8 #> 2 44.6 #> 3 66.9 #> 4 38.4 #> 5 39.8 #> 6 56.5 #> 7 45.5 #> 8 47.3 #> 9 41.4 #> 10 39.2 #> # ℹ 9,990 more rows"},{"path":"http://svmiller.com/reference/rd_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual Density Plot for Linear Models — rd_plot","title":"Residual Density Plot for Linear Models — rd_plot","text":"rd_plot() provides visual diagnostic normality assumption linear model. Provided OLS model fit lm() base R, function extracts residuals model creates density plot residuals (solid black line) standard normal distribution mean 0 standard deviation matching standard deviation residuals model. function may used diagnostic purposes.","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual Density Plot for Linear Models — rd_plot","text":"","code":"rd_plot(mod)"},{"path":"http://svmiller.com/reference/rd_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual Density Plot for Linear Models — rd_plot","text":"mod fitted linear model","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual Density Plot for Linear Models — rd_plot","text":"rd_plot() returns density plot ggplot2 object. density plot actual residuals solid black line. stylized normal distribution matching description residuals blue dashed line.","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Residual Density Plot for Linear Models — rd_plot","text":"user can always add ggplot2 elements top greater legibility/clarity. example, density plots can finicky making observations appear . Perhaps adjusting scale x ad hoc, fact, may warranted. goal function emphasize many real world applications, normality assumption residuals never held can often reasonably approximated upon visual inspection.","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Residual Density Plot for Linear Models — rd_plot","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/rd_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Residual Density Plot for Linear Models — rd_plot","text":"","code":"M1 <- lm(mpg ~ ., data=mtcars) rd_plot(M1)"},{"path":"http://svmiller.com/reference/revcode.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverse code a numeric variable — revcode","title":"Reverse code a numeric variable — revcode","text":"revcode allows reverse code numeric variable. , say, Likert item values 1, 2, 3, 4, 5, function inverts scale 1 = 5, 2 = 4, 3 = 3, 4 = 2, 5 = 1.","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverse code a numeric variable — revcode","text":"","code":"revcode(x)"},{"path":"http://svmiller.com/reference/revcode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverse code a numeric variable — revcode","text":"x numeric vector","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverse code a numeric variable — revcode","text":"function returns numeric vector reverse codes numeric vector supplied .","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Reverse code a numeric variable — revcode","text":"function passes NAs may variable. assume, reasonably might add, observed values include minimum maximum. usually case discrete ordered-categorical variable (like Likert item). also assumes numeric vector supplied contains possible values minimum observed value 1. usually safe assumption survey data variable interest ordinal (either 1:4 scale, 1:5 scale, 1:10 scale). matter, use function mind.","code":""},{"path":"http://svmiller.com/reference/revcode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverse code a numeric variable — revcode","text":"","code":"data.frame(x1 = rep(c(1:7, NA), 2), x2 = c(1:10, 1:4, NA, NA), x3 = rep(c(1:4), 4)) -> example_data library(dplyr) library(magrittr) example_data %>% mutate_at(vars(\"x1\", \"x2\", \"x3\"), ~revcode(.)) #> x1 x2 x3 #> 1 7 10 4 #> 2 6 9 3 #> 3 5 8 2 #> 4 4 7 1 #> 5 3 6 4 #> 6 2 5 3 #> 7 1 4 2 #> 8 NA 3 1 #> 9 7 2 4 #> 10 6 1 3 #> 11 5 10 2 #> 12 4 9 1 #> 13 3 8 4 #> 14 2 7 3 #> 15 1 NA 2 #> 16 NA NA 1"},{"path":"http://svmiller.com/reference/sbayesboot.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"sbayesboot() performs Bayesian bootstrap regression model.","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"","code":"sbayesboot(object, reps = 1000L, seed, cluster = NULL, ...)"},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"object regression model object reps many bootstrap replicates user wants. Defaults 1000 seed set optional seed reproducibility cluster optional cluster calibrating weights ... optional arguments","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"sbayesboot() takes fitted regression model returns matrix bootstrapped coefficients (intercept). easily converted data frame ease summary.","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"code underpinning sbayesboot() largely derived code provided Grant McDermott Vincent Arel-Bundock. approach takes flexibility McDermott's model-agnostic code (along ease specifying clusters) combines Arel-Bundock's update() approach actual bootstrapping. may screwed something , feel free point cases screw .","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"Grant McDermott, Vincent Arel-Bundock","code":""},{"path":"http://svmiller.com/reference/sbayesboot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Bootstrap a Regression Model, the Bayesian Way — sbayesboot","text":"","code":"# \\donttest{ M1 <- lm(mpg ~ disp + wt + hp, mtcars) # Default options BB1 <- sbayesboot(M1) # Cluster bootstrap on cylinder variable BB2 <- sbayesboot(M1, cluster=~cyl) # }"},{"path":"http://svmiller.com/reference/sbtscs.html","id":null,"dir":"Reference","previous_headings":"","what":"Create ","title":"Create ","text":"sbtscs() allows create spells (\"peace years\" international conflict context) observations event. allow researcher better model temporal dependence binary time-series cross-section (\"BTSCS\") models.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create ","text":"","code":"sbtscs(data, event, tvar, csunit, pad_ts = FALSE)"},{"path":"http://svmiller.com/reference/sbtscs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create ","text":"data data set working event event (0, 1) want spells peace years tvar time variable (e.g. year) csunit cross-sectional unit (likely dyad boilerplate international conflict stuff) pad_ts time-series filled panels unbalanced/gaps? Defaults FALSE.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create ","text":"sbtscs() takes data frame returns data frame new variable named spell.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create ","text":"confess outright, obvious anyone looks code, liberally copy Dave Armstrong's btscs() function DAMisc package. offer two improvements. One, btscs() function chokes large number cross-sectional units recorded \"event.\" know happens . , \"tidying\" code leaning dplyr substantially speeds computation. Incidentally, concerns cross-sectional units recorded events can choke btscs() function large numbers.","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create ","text":"Armstrong, Dave. 2016. “DAMisc: Dave Armstrong's Miscellaneous Functions.” R package version 1.4-3. Miller, Steven V. 2017. “Quickly Create Peace Years BTSCS Models sbtscs stevemisc.” http://svmiller.com/blog/2017/06/quickly-create-peace-years--btscs-models--stevemisc/","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create ","text":"David . Armstrong, Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/sbtscs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create ","text":"","code":"if (FALSE) { # \\dontrun{ library(dplyr) library(stevemisc) data(usa_mids) # notice: no quotes sbtscs(usa_mids, midongoing, year, dyad) } # }"},{"path":"http://svmiller.com/reference/show_ranef.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a caterpillar plot of random effects from a mixed model — show_ranef","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"show_ranef() allows user estimating mixed model quickly plot random intercepts (conditional variances) given random effect mixed model. cases random slope intercept, function plots random slope another caterpillar plot (another facet)","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"","code":"show_ranef(model, group, reorder = TRUE)"},{"path":"http://svmiller.com/reference/show_ranef.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"model fitted mixed model random intercepts group random intercept/slopes want see caterpillar plot? Declare character reorder optional argument. DEFAULT TRUE, “re-orders” intercepts original value data. FALSE, ensuing caterpillar plot defaults default method ordering levels random effect estimated conditional mode.","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"show_ranef() returns caterpillar plot random intercepts given mixed model. broom.mixed::augment() can process , function work just fine.","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"function simple wrapper broom.mixed , obviously ggplot2 heavy lifting.","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/show_ranef.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a caterpillar plot of random effects from a mixed model — show_ranef","text":"","code":"library(lme4) #> Loading required package: Matrix library(stevemisc) data(sleepstudy) M1 <- lmer(Reaction ~ Days + (Days | Subject), data=sleepstudy) show_ranef(M1, \"Subject\") show_ranef(M1, \"Subject\", reorder=FALSE)"},{"path":"http://svmiller.com/reference/smvrnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate from a Multivariate Normal Distribution — smvrnorm","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"smvrnorm() simulates data multivariate normal distribution.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"","code":"smvrnorm( n = 1, mu, sigma, tol = 1e-06, empirical = FALSE, eispack = FALSE, seed )"},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"n number observations simulate mu vector means sigma positive-definite symmetric matrix specifying covariance matrix variables. tol tolerance (relative largest variance) numerical lack positive-definiteness sigma. empirical logical. true, mu sigma specify empirical population mean covariance matrix. eispack logical. values FALSE result error seed set optional seed","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"function returns simulated data multivariate normal distribution.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"simple port rename mvrnorm() MASS package. elect plagiarize/port MASS package conflicts lot things workflow, especially select(). useful \"informal Bayes\" approaches generating quantities interest regression model.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"B. D. Ripley (1987) Stochastic Simulation. Wiley. Page 98.","code":""},{"path":"http://svmiller.com/reference/smvrnorm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate from a Multivariate Normal Distribution — smvrnorm","text":"","code":"M1 <- lm(mpg ~ disp + cyl, mtcars) smvrnorm(100, coef(M1), vcov(M1)) #> (Intercept) disp cyl #> [1,] 34.44180 -0.016093590 -1.6668364 #> [2,] 32.03398 -0.018701730 -1.2757296 #> [3,] 37.36271 -0.020331480 -2.0825009 #> [4,] 36.71988 -0.012678111 -2.3042494 #> [5,] 34.98588 -0.030837751 -1.2148439 #> [6,] 35.75183 -0.031857450 -1.3338967 #> [7,] 36.17367 -0.006791696 -2.3472860 #> [8,] 32.91489 -0.026496439 -1.0494384 #> [9,] 36.29545 -0.026003203 -1.5875640 #> [10,] 34.96627 -0.016327081 -1.7916997 #> [11,] 30.70833 -0.034137268 -0.4632021 #> [12,] 35.25495 -0.024852183 -1.5871001 #> [13,] 29.67764 -0.045307263 0.1279449 #> [14,] 33.27457 -0.024805958 -1.2678062 #> [15,] 35.90700 -0.028802646 -1.3244104 #> [16,] 32.70040 -0.028711966 -0.8978397 #> [17,] 35.64675 -0.007246586 -2.2356900 #> [18,] 35.50407 -0.023854934 -1.6782045 #> [19,] 33.96691 -0.030668985 -1.2353990 #> [20,] 37.86235 -0.004940957 -2.7436385 #> [21,] 33.28914 -0.012421275 -1.7446521 #> [22,] 34.30010 -0.028712418 -1.3577551 #> [23,] 36.06222 -0.021116329 -1.8633183 #> [24,] 37.46621 -0.023343183 -1.8936744 #> [25,] 36.07328 -0.021921595 -1.8317841 #> [26,] 36.56082 -0.019457823 -1.8880890 #> [27,] 31.39368 -0.040165534 -0.4286164 #> [28,] 29.33698 -0.032083182 -0.4472746 #> [29,] 34.28392 -0.013035185 -1.8310718 #> [30,] 33.58597 -0.023962357 -1.3969869 #> [31,] 28.97209 -0.030077306 -0.3020914 #> [32,] 34.96147 -0.001392552 -2.3925628 #> [33,] 34.29185 -0.027030451 -1.3731181 #> [34,] 30.42862 -0.020690303 -0.8481803 #> [35,] 36.42266 -0.017422488 -1.9164967 #> [36,] 33.56155 -0.043038754 -0.5721694 #> [37,] 31.87355 -0.019477538 -1.2724780 #> [38,] 36.74445 -0.016871084 -1.9450795 #> [39,] 32.78354 -0.016504146 -1.5442419 #> [40,] 35.39997 -0.019395674 -1.8751268 #> [41,] 35.60957 -0.020148263 -1.6666933 #> [42,] 37.99107 0.007172362 -3.3032306 #> [43,] 40.85089 -0.010726402 -2.9515661 #> [44,] 37.95101 -0.028593076 -1.7685981 #> [45,] 33.67591 -0.022775762 -1.3301353 #> [46,] 37.97592 -0.015515732 -2.4204318 #> [47,] 33.96554 -0.019441511 -1.6081523 #> [48,] 34.18454 -0.019333806 -1.4407924 #> [49,] 34.55656 -0.025640883 -1.3469341 #> [50,] 33.92344 -0.014969830 -1.6643016 #> [51,] 34.34051 -0.020552987 -1.5917340 #> [52,] 34.10286 -0.032555955 -1.0019450 #> [53,] 35.99606 -0.016518191 -1.8187662 #> [54,] 31.72513 -0.029475953 -0.7907692 #> [55,] 32.81959 -0.022554931 -1.2138406 #> [56,] 35.99504 -0.028737140 -1.5838799 #> [57,] 33.62026 -0.012538926 -1.6514403 #> [58,] 37.26592 -0.022992995 -1.8182772 #> [59,] 36.14499 -0.017645647 -1.9345679 #> [60,] 35.23342 -0.004586844 -2.2137780 #> [61,] 31.81256 -0.043409009 -0.2694654 #> [62,] 36.43215 -0.022729756 -1.8196314 #> [63,] 39.28006 -0.004245869 -2.9370557 #> [64,] 34.91233 -0.032243438 -1.1931419 #> [65,] 31.46309 -0.015660756 -1.1197044 #> [66,] 34.22477 -0.025875124 -1.3415898 #> [67,] 37.68134 -0.027979933 -1.7807292 #> [68,] 33.35864 -0.017707105 -1.4843595 #> [69,] 33.76621 -0.021178795 -1.5660815 #> [70,] 38.65470 -0.020472447 -2.1995093 #> [71,] 30.48207 -0.028090660 -0.6645297 #> [72,] 32.25134 -0.034734893 -0.6216782 #> [73,] 30.90073 -0.028385834 -0.7434711 #> [74,] 33.09523 -0.033242923 -0.8108385 #> [75,] 35.14176 -0.019699942 -1.5724585 #> [76,] 37.28685 -0.012467155 -2.4016195 #> [77,] 36.72132 -0.008554207 -2.4195983 #> [78,] 36.02494 -0.024415962 -1.8528437 #> [79,] 34.11541 -0.031099366 -1.2083065 #> [80,] 34.19654 -0.020677674 -1.4154843 #> [81,] 30.07842 -0.025284376 -0.6767066 #> [82,] 37.29116 -0.018444192 -2.3045275 #> [83,] 38.45071 -0.010092197 -2.5877892 #> [84,] 33.82277 -0.023651190 -1.3520588 #> [85,] 34.66278 -0.014667477 -1.7998157 #> [86,] 31.40191 -0.032412551 -0.7281680 #> [87,] 42.25891 -0.009559488 -3.1434194 #> [88,] 34.35595 -0.006480486 -2.1190544 #> [89,] 36.11058 -0.010001272 -2.2122046 #> [90,] 33.28499 -0.012173669 -1.6930537 #> [91,] 34.92931 -0.018505094 -1.5643630 #> [92,] 35.37642 -0.025342419 -1.5472113 #> [93,] 35.41801 -0.013244018 -1.9558777 #> [94,] 36.00575 -0.022021632 -1.7105708 #> [95,] 33.56557 -0.031543846 -0.9619895 #> [96,] 34.27518 -0.026714387 -1.2335824 #> [97,] 36.98794 -0.003431031 -2.5075174 #> [98,] 38.92558 -0.018050955 -2.3751259 #> [99,] 37.27147 -0.015998570 -2.1360520 #> [100,] 30.62294 -0.033973310 -0.4645830"},{"path":"http://svmiller.com/reference/stevepubs.html","id":null,"dir":"Reference","previous_headings":"","what":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"data publications, barring things like book reviews forthcoming pieces. use data illustrate print_refs() function. cite publications .","code":""},{"path":"http://svmiller.com/reference/stevepubs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"","code":"stevepubs"},{"path":"http://svmiller.com/reference/stevepubs.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"data frame following 14 variables. CATEGORY entry type BIBTEXKEY unique entry key AUTHOR list authors entry BOOKTITLE book title, appropriate JOURNAL journal title, appropriate NUMBER journal volume number, appropriate PAGES range page numbers, appropriate PUBLISHER book publisher, appropriate TITLE title publication VOLUME journal volume number, appropriate YEAR year publication, character. Publications year assumed forthcoming DOI DOI, entered one","code":""},{"path":"http://svmiller.com/reference/stevepubs.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"An Incomplete List of My Publications, All of Which You Should Cite — stevepubs","text":"Cite publications , goons. Extremely Smokey Bear voice can jack h-index infinity.","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":null,"dir":"Reference","previous_headings":"","what":"Strategic Rivalries, 1494-2010 — strategic_rivalries","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"simple summary strategic (inter-state) rivalries Thompson Dreyer (2012).","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"","code":"data(\"strategic_rivalries\")"},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"data frame 197 observations following 10 variables. rivalryno numeric vector rivalry number rivalryname character vector rivalry name sidea character vector first country rivalry sideb character vector second country rivalry styear numeric vector start year rivalry endyear numeric vector end year rivalry region character vector region rivalry, per Thompson Dreyer (2012) type1 character vector primary type rivalry (spatial, positional, ideological, interventionary) type2 character vector secondary type rivalry, applicable (spatial, positional, ideological, interventionary) type3 character vector tertiary type rivalry, applicable (spatial, positional, ideological, interventionary)","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"Information gathered appendix Thompson Dreyer (2012). Ongoing rivalries right-bound 2010, date publication Thompson Dreyer's handbook. Users free change like.","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"Thompson, William R. David Dreyer. 2012. Handbook International Rivalries. CQ Press.","code":""},{"path":"http://svmiller.com/reference/strategic_rivalries.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Strategic Rivalries, 1494-2010 — strategic_rivalries","text":"","code":"data(strategic_rivalries)"},{"path":"http://svmiller.com/reference/studentt.html","id":null,"dir":"Reference","previous_headings":"","what":"The Student-t Distribution (Location-Scale) — studentt","title":"The Student-t Distribution (Location-Scale) — studentt","text":"density, distribution function, quantile function random generation Student-t distribution location mu, scale sigma, degrees freedom df. Base R gives -called \"standard\" Student-t distribution, just varying degrees freedom. generalizes standard Student-t three-parameter version.","code":""},{"path":"http://svmiller.com/reference/studentt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"The Student-t Distribution (Location-Scale) — studentt","text":"","code":"dst(x, df, mu, sigma) pst(q, df, mu, sigma) qst(p, df, mu, sigma) rst(n, df, mu, sigma)"},{"path":"http://svmiller.com/reference/studentt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"The Student-t Distribution (Location-Scale) — studentt","text":"x, q vector quantiles df vector degrees freedom mu vector location value sigma vector scale values p Vector probabilities. n Number samples draw distribution.","code":""},{"path":"http://svmiller.com/reference/studentt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"The Student-t Distribution (Location-Scale) — studentt","text":"dst() returns density. pst() returns distribution function. qst() returns quantile function. rst() returns random numbers.","code":""},{"path":"http://svmiller.com/reference/studentt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The Student-t Distribution (Location-Scale) — studentt","text":"simple hack taken Wikipedia. itch wanting scratch . can probably generalize outward allow tail log stuff, wrote mostly random number generation. Right now, written account fact sigma non-negative, user know (now).","code":""},{"path":[]},{"path":"http://svmiller.com/reference/tbl_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert data frame to an object of class ","title":"Convert data frame to an object of class ","text":"tbl_df() ensures legacy compatibility scripts since function deprecated dplyr. to_tbl() also added fun.","code":""},{"path":"http://svmiller.com/reference/tbl_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert data frame to an object of class ","text":"","code":"tbl_df(...) to_tbl(...)"},{"path":"http://svmiller.com/reference/tbl_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert data frame to an object of class ","text":"... optional parameters, put anything . just quell CRAN checks.","code":""},{"path":"http://svmiller.com/reference/tbl_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert data frame to an object of class ","text":"function takes data frame turns tibble.","code":""},{"path":"http://svmiller.com/reference/tbl_df.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert data frame to an object of class ","text":"","code":"tbl_df(mtcars) #> Warning: `tbl_df()` was deprecated in dplyr 1.0.0. #> ℹ Please use `tibble::as_tibble()` instead. #> # A tibble: 32 × 11 #> mpg cyl disp hp drat wt qsec vs am gear carb #> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # ℹ 22 more rows tbl_df(iris) #> Warning: `tbl_df()` was deprecated in dplyr 1.0.0. #> ℹ Please use `tibble::as_tibble()` instead. #> # A tibble: 150 × 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # ℹ 140 more rows"},{"path":"http://svmiller.com/reference/usa_mids.html","id":null,"dir":"Reference","previous_headings":"","what":"United States Militarized Interstate Disputes (MIDs) — usa_mids","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"non-directed dyad-year data set militarized interstate disputes involving United States. created illustrate sbtscs() function.","code":""},{"path":"http://svmiller.com/reference/usa_mids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"","code":"usa_mids"},{"path":"http://svmiller.com/reference/usa_mids.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"data frame 14586 observations following 6 variables. dyad unique identifier dyad ccode1 Correlates War state code United States (2) ccode2 Correlates War state code state dyad year observation year dyad midongoing ongoing inter-state dispute dyad-year? midonset new inter-state dispute onset dyad-year","code":""},{"path":"http://svmiller.com/reference/usa_mids.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"Data generated time ago. Rare cases multiple disputes ongoing given dyad-year first whittled isolating 1) unique dispute onsets. Thereafter, data select 2) highest fatality, 3) highest hostility level, 4) longer dispute, 5) just picking whichever one came first. duplicate non-directed dyad-year observations.","code":""},{"path":"http://svmiller.com/reference/usa_mids.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"United States Militarized Interstate Disputes (MIDs) — usa_mids","text":"Gibler, Douglas M., Steven V. Miller, Erin K. Little. 2016. “Analysis Militarized Interstate Dispute (MID) Dataset, 1816-2001.” International Studies Quarterly 60(4): 719-730.","code":""},{"path":"http://svmiller.com/reference/wls.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Weighted Least Squares of Your OLS Model — wls","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"wls() takes OLS model re-estimates using weighted least squares approach. Weighted least squares often \"textbook\" approach dealing presence heteroskedastic standard errors, weighted least squares estimates compared OLS estimates uncertainty check consistency potential inferential implications.","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"","code":"wls(mod)"},{"path":"http://svmiller.com/reference/wls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"mod fitted OLS model","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"wls() returns new model object weighted least squares re-estimation OLS model supplied .","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"function robust potential model specification oddities (e.g. polynomials fixed effects). also perform nicely presence missing data, na.action = na.exclude supplied first offending OLS model supplied function weighted least squares re-estimation.","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"Steven V. Miller","code":""},{"path":"http://svmiller.com/reference/wls.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Weighted Least Squares of Your OLS Model — wls","text":"","code":"M1 <- lm(mpg ~ ., data=mtcars) M2 <- wls(M1) summary(M2) #> #> Call: #> lm(formula = mpg ~ cyl + disp + hp + drat + wt + qsec + vs + #> am + gear + carb, data = A, weights = wts) #> #> Weighted Residuals: #> Min 1Q Median 3Q Max #> -1.7522 -0.8385 -0.2326 0.9062 2.7257 #> #> Coefficients: #> Estimate Std. Error t value Pr(>|t|) #> (Intercept) 16.798050 19.371537 0.867 0.396 #> cyl -0.394419 1.021128 -0.386 0.703 #> disp 0.002299 0.015834 0.145 0.886 #> hp -0.009795 0.020927 -0.468 0.645 #> drat 0.934691 1.646690 0.568 0.576 #> wt -2.383075 1.748083 -1.363 0.187 #> qsec 0.552577 0.760879 0.726 0.476 #> vs -0.124255 2.231573 -0.056 0.956 #> am 2.236543 2.158780 1.036 0.312 #> gear 0.484887 1.520711 0.319 0.753 #> carb -0.564120 0.802962 -0.703 0.490 #> #> Residual standard error: 1.518 on 21 degrees of freedom #> Multiple R-squared: 0.8621,\tAdjusted R-squared: 0.7965 #> F-statistic: 13.13 on 10 and 21 DF, p-value: 6.318e-07 #>"},{"path":"http://svmiller.com/reference/wom.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate Week of the Month from a Date — wom","title":"Generate Week of the Month from a Date — wom","text":"wom() convenience function use constructing calendars ggplot2. takes date returns, numeric vector, week month date given .","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate Week of the Month from a Date — wom","text":"","code":"wom(x)"},{"path":"http://svmiller.com/reference/wom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate Week of the Month from a Date — wom","text":"x date","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate Week of the Month from a Date — wom","text":"wom() convenience function use constructing calendars ggplot2. takes date returns, numeric vector, week month date given .","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate Week of the Month from a Date — wom","text":"wom() assumes Sunday start week. can assuredly customized later function, right now assumption Sunday start week (Monday, might contexts).","code":""},{"path":"http://svmiller.com/reference/wom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate Week of the Month from a Date — wom","text":"","code":"wom(as.Date(\"2022-01-01\")) #> [1] 1 wom(Sys.Date()) #> [1] 5"},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-190","dir":"Changelog","previous_headings":"","what":"stevemisc 1.9.0","title":"stevemisc 1.9.0","text":"rewb_at() convenience wrapper mean_at(), group_mean_center_at(), center_at(). ’s useful preparing data random effects, within-(REWB) panel analysis. linloess_plot() now special print class suppressing warnings come LOESS smoother. Additionally, suppress_warnings argument function.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-180","dir":"Changelog","previous_headings":"","what":"stevemisc 1.8.0","title":"stevemisc 1.8.0","text":"CRAN release: 2024-08-23 rd_plot() now na.rm = TRUE argument quietly passed extraction standard deviation residuals. ensures missing values data don’t result missing residuals, result standard deviation residuals. linloess_plot() now resid argument allows comparison model’s residuals y-axis rather default (raw values y y-axis). Assorted documentation fixes.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-170","dir":"Changelog","previous_headings":"","what":"stevemisc 1.7.0","title":"stevemisc 1.7.0","text":"CRAN release: 2023-11-06 Add charitable_contributions. Add rd_plot() Scoped helper verbs (“” functions) gradually getting .support , , breaking link superseded _at() functions dplyr. linloess_plot() now se argument optionally disabling standard error bands. particularly ill-fitting linear models, may advisable.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-160","dir":"Changelog","previous_headings":"","what":"stevemisc 1.6.0","title":"stevemisc 1.6.0","text":"CRAN release: 2023-03-22 theme_steve() removed package. function now stevethemes, house ggplot2 themes going forward. Fix warning/error/bug ps_spells() brought attention CRAN. don’t know came just now, ’s apparently issue lurking around R development time now ’s always wrong call order() data frame. underlying order() calls replaced arrange(). fix concerns related issue also affects peacesciencer.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-150","dir":"Changelog","previous_headings":"","what":"stevemisc 1.5.0","title":"stevemisc 1.5.0","text":"CRAN release: 2023-02-01 Package now contains scoped helper verbs—-called “” functions. functions—like center_at(), diff_at(), —self-contained one R Documentation file. theme_steve_ms() now actually uses “Crimson Pro”, “Crimson Text”. theme_steve() deprecated removed later release. function effectively moved stevethemes, also expanded improved. remaining ggplot2 functions package becoming legacy functions mind. wls() weighted least squares re-estimations OLS model. HT @hadley information class issue. fct_reorg() completely re-written (@hadley ) light new forcats release.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-141","dir":"Changelog","previous_headings":"","what":"stevemisc 1.4.1","title":"stevemisc 1.4.1","text":"CRAN release: 2022-04-12 Adjust filter_refs() print_refs() longer require bib2df. , bib2df also removed package dependency.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-140","dir":"Changelog","previous_headings":"","what":"stevemisc 1.4.0","title":"stevemisc 1.4.0","text":"CRAN release: 2022-03-23 Add filter_refs() , , bib2df package dependency. print_refs() now work (implied) bib2df data frame .bib entries. Add wom(). Add sbayesboot(). Add map_quiz. Update stevepubs. Update show_ranef(), longer requires broom.mixed underneath hood. Remove broom.mixed package dependency.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-130","dir":"Changelog","previous_headings":"","what":"stevemisc 1.3.0","title":"stevemisc 1.3.0","text":"CRAN release: 2021-10-22 Add data set French leaders (fra_leaderyears). data set stress-testing peace spell calculations cross-sectional units decidedly imbalanced. Add data set German dyad-years (gmy_dyadyears). data set stress-testing peace spell calculations huge gap data. Add ps_spells(), general spell calculations going forward. Add linloess_plot(). , add tidyr dependency.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-120","dir":"Changelog","previous_headings":"","what":"stevemisc 1.2.0","title":"stevemisc 1.2.0","text":"CRAN release: 2021-07-27 Add prepare_refs() print_refs() Add r2sd_at(). Add revcode(). Add stevepubs. Add theme_steve_ms() theme_steve_font().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-110","dir":"Changelog","previous_headings":"","what":"stevemisc 1.1.0","title":"stevemisc 1.1.0","text":"CRAN release: 2021-06-14 Add ps_btscs() future use peacesciencer. Moved Imports: entries Suggests: CRAN compliance. import packages (DBI, RSQLite, dbplyr) concern db_lselect() function.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-100","dir":"Changelog","previous_headings":"","what":"stevemisc 1.0.0","title":"stevemisc 1.0.0","text":"CRAN release: 2021-04-19 slated first professional/public release CRAN. Package features major updates functions, mostly CRAN compliance. New features include fct_reorg(), gvi() shortcut get_var_info(), ess9_labelled data illustration, scale-location t-distribution functions, .","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-031","dir":"Changelog","previous_headings":"","what":"stevemisc 0.3.1","title":"stevemisc 0.3.1","text":"Move almost data stevedata. Add p_z().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-030","dir":"Changelog","previous_headings":"","what":"stevemisc 0.3.0","title":"stevemisc 0.3.0","text":"Mostly cosmetic fixes functionality things. CRAN compliant.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-022","dir":"Changelog","previous_headings":"","what":"stevemisc 0.2.2","title":"stevemisc 0.2.2","text":"Add usa_mids. Update sbtscs(). Add vignette.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-02","dir":"Changelog","previous_headings":"","what":"stevemisc 0.2","title":"stevemisc 0.2","text":"Update carrec(), cor2data(), corvectors(), get_sims(), get_var_info(), make_perclab(), make_scale(), jenny(), %nin%, normal_dist(), rbnorm(), sbtscs(), show_ranef(), smvrnorm(), theme_steve(), theme_steve_web(). Remove multiplot().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0117","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.17","title":"stevemisc 0.1.17","text":"Update fakeAPI.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0116","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.16","title":"stevemisc 0.1.16","text":"Add seed corvectors(). Add fakeAPI.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0114","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.14","title":"stevemisc 0.1.14","text":"Add corvectors() jenny().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0113","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.13","title":"stevemisc 0.1.13","text":"Add tbl_df() to_tbl(). Update theme_steve_web(). Thanks @mewdewitt suggestions.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0111","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.11","title":"stevemisc 0.1.11","text":"Add %nin%.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0110","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.10","title":"stevemisc 0.1.10","text":"Add smvrnorm().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-018","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.8","title":"stevemisc 0.1.8","text":"Generalize get_sims() handle non-mixed models.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0173","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7.3","title":"stevemisc 0.1.7.3","text":"Update States.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0172","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7.2","title":"stevemisc 0.1.7.2","text":"Update DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0171","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7.1","title":"stevemisc 0.1.7.1","text":"Add seed rbnorm().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-017","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.7","title":"stevemisc 0.1.7","text":"Add normal_dist(), States, update Presidents.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0169","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.9","title":"stevemisc 0.1.6.9","text":"Remove Presidents.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0168","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.8","title":"stevemisc 0.1.6.8","text":"Add ESS9GB Presidents.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0166","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.6","title":"stevemisc 0.1.6.6","text":"Add Arca.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0165","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.5","title":"stevemisc 0.1.6.5","text":"Update aluminum_premiums DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0164","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.4","title":"stevemisc 0.1.6.4","text":"Add asn_stats DST.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0162","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.2","title":"stevemisc 0.1.6.2","text":"Add cor2data().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0161","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.1","title":"stevemisc 0.1.6.1","text":"Add select z-values vectors.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01601","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.6.01","title":"stevemisc 0.1.6.01","text":"Add rbnorm().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0159","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.9","title":"stevemisc 0.1.5.9","text":"Update aluminum_premiums.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0158","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.8","title":"stevemisc 0.1.5.8","text":"Add strategic_rivalries.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0157","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.7","title":"stevemisc 0.1.5.7","text":"Add sugar_prices.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0156","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.6","title":"stevemisc 0.1.5.6","text":"Add post_bg().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0155","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.5","title":"stevemisc 0.1.5.5","text":"Add ghp100k.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0154","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.4","title":"stevemisc 0.1.5.4","text":"Add eustates multiplot().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0152","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.2","title":"stevemisc 0.1.5.2","text":"Add get_sims(). Update theme_steve_web().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0151","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5.1","title":"stevemisc 0.1.5.1","text":"Add r2sd().","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-015","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.5","title":"stevemisc 0.1.5","text":"Add carrec() cardkrieger1994mwe.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01496","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.6","title":"stevemisc 0.1.4.9.6","text":"Add clemsontemps, gss_abortion, map_quiz.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01495","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.5","title":"stevemisc 0.1.4.9.5","text":"Add nesarc_drinkspd.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01493","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.3","title":"stevemisc 0.1.4.9.3","text":"Add usa_chn_gdp_forecasts.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01492","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.2","title":"stevemisc 0.1.4.9.2","text":"Add imf_coffee_data.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01491","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9.1","title":"stevemisc 0.1.4.9.1","text":"Add recessions.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0149","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.9","title":"stevemisc 0.1.4.9","text":"Add ukg_eeri.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01489","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.9","title":"stevemisc 0.1.4.8.9","text":"Rename edq_passengercars eq_passengercars.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01488","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.8","title":"stevemisc 0.1.4.8.8","text":"Add edq_passengercars.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01487","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.7","title":"stevemisc 0.1.4.8.7","text":"Update documentation migrants_usa mvprod.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01486","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.6","title":"stevemisc 0.1.4.8.6","text":"Add mvprod.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01485","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.5","title":"stevemisc 0.1.4.8.5","text":"Update documentation migrants_usa.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01484","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.4","title":"stevemisc 0.1.4.8.4","text":"Update documentation migrants_usa.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01483","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.3","title":"stevemisc 0.1.4.8.3","text":"Add migrants_usa.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01482","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.2","title":"stevemisc 0.1.4.8.2","text":"Update steve_clothes.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-01481","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8.1","title":"stevemisc 0.1.4.8.1","text":"Update DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0148","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.8","title":"stevemisc 0.1.4.8","text":"Add DJIA.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0147","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.7","title":"stevemisc 0.1.4.7","text":"Add aluminum_premiums.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0146","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.6","title":"stevemisc 0.1.4.6","text":"Update theme_steve(), theme_steve_web(), ustradegdp.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0144","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.4","title":"stevemisc 0.1.4.4","text":"Add ustradegdp.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0143","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.3","title":"stevemisc 0.1.4.3","text":"Add steves_clothes.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0142","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.2","title":"stevemisc 0.1.4.2","text":"Add several data sets: articseaice, co2data, osu_results, sealevels.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-0141","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4.1","title":"stevemisc 0.1.4.1","text":"Fix dplyr NAMESPACE issue,thanks David Armstrong recommending .","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-014","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.4","title":"stevemisc 0.1.4","text":"Add get_var_info(), theme_steve_web2(), fonts. inst/fonts directory.","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-013","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.3","title":"stevemisc 0.1.3","text":"Add theme_steve_web()","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-012","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.2","title":"stevemisc 0.1.2","text":"Changed title theme_steve(). Add mround2()","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-011","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.1","title":"stevemisc 0.1.1","text":"Changed title theme_steve()","code":""},{"path":"http://svmiller.com/news/index.html","id":"stevemisc-010","dir":"Changelog","previous_headings":"","what":"stevemisc 0.1.0","title":"stevemisc 0.1.0","text":"Initial developmental release. Features included: sbtscs() show_ranef() theme_steve()","code":""}] diff --git a/man/linloess_plot.Rd b/man/linloess_plot.Rd index 1603023..b62ba44 100644 --- a/man/linloess_plot.Rd +++ b/man/linloess_plot.Rd @@ -3,7 +3,7 @@ \name{linloess_plot} \alias{linloess_plot} \alias{print.linloess} -\title{Compare Linear Smoother to LOESS Smoother for Your OLS Model} +\title{Compare Linear Smoother to LOESS Smoother for Your Linear Model} \usage{ linloess_plot( mod, @@ -18,7 +18,7 @@ linloess_plot( \method{print}{linloess}(x, ...) } \arguments{ -\item{mod}{a fitted OLS model} +\item{mod}{a fitted model, ideally a simple linear model} \item{resid}{logical, defaults to \code{TRUE}. If \code{FALSE}, the y-axis on these plots are the raw values of the dependent variable. If \code{TRUE}, @@ -40,7 +40,7 @@ sparse pockets of the data.} the plot suppresses assorted warnings from the LOESS smoother that would otherwise be cautioning you about things your eyes could otherwise see.} -\item{...}{additional arguments (ignored)} +\item{...}{Additional arguments in the context of the print function (not used)} \item{x}{a ggplot object with this special 'linloess' class} } @@ -50,18 +50,18 @@ otherwise be cautioning you about things your eyes could otherwise see.} standard error bands) and the LOESS smoother is a dashed black line (with gray/default standard error bands). You can add cosmetic features to it after the fact. The function may spit warnings to you related to the LOESS smoother, -depending your data. I think these to be fine the extent to which this is -really just a visual aid and an informal diagnostic for the linearity -assumption. +depending your data and whether you have disabled the warnings in the +function. I think these to be fine the extent to which this is really just a +visual aid and an informal diagnostic for the linearity assumption. } \description{ \code{linloess_plot()} provides a visual diagnostic of the -linearity assumption of the OLS model. Provided an OLS model fit by +linearity assumption of the OLS model. Provided a linear model fit by \code{lm()} in base R, the function extracts the model frame and creates a faceted scatterplot. For each facet, a linear smoother and LOESS smoother are estimated over the points. Users who run this function can assess just how much the linear smoother and LOESS smoother diverge. The more they -diverge, the more the user can determine how much the OLS model is a good +diverge, the more the user can determine how much the linear model is a good fit as specified. The plot will also point to potential outliers that may need further consideration. }