add

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: stevemisc
 Type: Package
 Title: Steve's Miscellaneous Functions
-Version: 1.6.9
+Version: 1.7.0
 Depends: R (>= 3.6.0), stats
 Authors@R: c(
     person("Steve", "Miller", email = "[email protected]",

NAMESPACE

@@ -111,6 +111,7 @@ importFrom(stats, "nobs")
 importFrom("stats", "complete.cases", "update", "rexp")
 importFrom(tidyr, "unite")
 
-# This is for wls()
+# This is for wls() and rd_plot()
+
+importFrom("stats", "lm", "na.exclude", "resid")
 
-importFrom("stats", "lm", "na.exclude")

NEWS.md

@@ -1,7 +1,9 @@
 # stevemisc 1.7.0
 
 - Add `charitable_contributions`.
+- Add `rd_plot()`
 - Scoped helper verbs (the "at" functions) are gradually getting `.by` support and, with it, breaking their link to the superseded `_at()` functions from `{dplyr}`. 
+- `linloess_plot()` now has a `se` argument for optionally disabling standard error bands. For particularly ill-fitting linear models, this may be advisable.
 
 # stevemisc 1.6.0
 

R/linloess_plot.R

@@ -10,6 +10,9 @@
 #' @details This function makes an implicit assumption that there is no variable in the regression
 #'  formula with the name ".y".
 #'
+#' It may be in your interest (for the sake of rudimentary diagnostic checks) to
+#' disable the standard error bands for particularly ill-fitting linear models.
+#'
 #' @return \code{linloess_plot()} returns a faceted scatterplot as a \pkg{ggplot2} object. The linear smoother is in solid blue (with blue
 #' 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
@@ -18,6 +21,7 @@
 #' @author Steven V. Miller
 #'
 #' @param mod a fitted OLS model
+#' @param se logical, defaults to \code{TRUE}. If \code{TRUE}, gives standard error estimates with the assorted smoothers.
 #' @param ... optional parameters, passed to the scatterplot (\code{geom_point()}) component of this function. Useful if you want to make the smoothers more legible against the points.
 #'
 #' @examples
@@ -28,7 +32,7 @@
 #' linloess_plot(M1, color="black", pch=21)
 
 
-linloess_plot <- function(mod, ...) {
+linloess_plot <- function(mod, se = TRUE, ...) {
   modframe <- model.frame(mod)
 
   dat <- gather(modframe, "var", "value", 2:ncol(modframe))
@@ -41,8 +45,9 @@ linloess_plot <- function(mod, ...) {
     # scatterplot
     geom_point(...) +
     # linear smoother
-    geom_smooth(method="lm", fill="blue") +
+    geom_smooth(method="lm", fill="blue", se = se) +
     # loess smoother, with different color
-    geom_smooth(method="loess", color="black", linetype="dashed")
+    geom_smooth(method="loess", color="black", linetype="dashed",
+                se = se)
 
 }

R/rd_plot.R

@@ -0,0 +1,48 @@
+#' Residual Density Plot for Linear Models
+#'
+#' @description \code{rd_plot()} provides a visual diagnostic of the normality
+#'  assumption of the linear model. Provided an OLS model fit by \code{lm()} in
+#'  base R, the function extracts the residuals of the model and creates
+#'  a density plot of those residuals (solid black line) against a standard
+#'  normal distribution with a mean of 0 and a standard deviation matching the
+#'  standard deviation of the residuals from the model. The function may be used
+#'  for diagnostic purposes.
+#'
+#' @details The user can always add \pkg{ggplot2} elements on top of this for
+#' greater legibility/clarity. For example, density plots can be finicky about
+#' making observations appear where they don't. Perhaps adjusting the scale
+#' of \code{x} ad hoc, after the fact, may be warranted.
+#'
+#' The goal of this function is to emphasize that in many real world applications,
+#' the normality assumption of the residuals is never held but can often be
+#' reasonably approximated upon visual inspection.
+#'
+#' @return \code{rd_plot()} returns a density plot a \pkg{ggplot2} object. A
+#' density plot of the actual residuals is a solid black line. A stylized normal
+#' distribution matching the description of the residuals is the blue dashed
+#' line.
+#'
+#' @author Steven V. Miller
+#'
+#' @param mod a fitted linear model
+#'
+#' @examples
+#'
+#' M1 <- lm(mpg ~ ., data=mtcars)
+#'
+#' rd_plot(M1)
+
+
+rd_plot <- function(mod) {
+
+  sdr <- sd(resid(mod))
+
+  hold_this <- data.frame(x = resid(mod))
+
+  ggplot(hold_this, aes(x)) +
+    geom_density() +
+    stat_function(fun = dnorm, color="blue",
+                  args = list(mean = 0, sd = sdr),
+                  linetype="dashed", linewidth=1.1)
+
+}

_pkgdown.yml

@@ -40,6 +40,7 @@ reference:
       - r1sd
       - r2sd
       - rbnorm
+      - rd_plot
       - revcode
       - sbayesboot
       - sbtscs

docs/pkgdown.yml

@@ -1,8 +1,8 @@
-pandoc: 2.19.2
+pandoc: 3.1.1
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2023-05-05T13:01Z
+last_built: 2023-11-06T08:18Z
 urls:
   reference: http://svmiller.com/reference
   article: http://svmiller.com/articles