update

NEWS.md

@@ -1,6 +1,8 @@
 # stevemisc 1.8.0
 
 - `rd_plot()` now has an `na.rm = TRUE` argument quietly passed to the extraction of the standard deviation of the residuals. This ensures that missing values in the data don't result in missing residuals, which then result in no standard deviation of the residuals.
+- `linloess_plot()` now has a `resid` argument that allows for comparison against the model's residuals on the *y*-axis rather than the default (the raw values of *y* on the *y*-axis).
+- Assorted documentation fixes.
 
 # stevemisc 1.7.0
 

R/linloess_plot.R

@@ -1,28 +1,42 @@
 #' Compare Linear Smoother to LOESS Smoother for Your OLS Model
 #'
-#' @description \code{linloess_plot()} provides a visual diagnostic of the linearity assumption of the OLS model.
-#'  Provided an OLS 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 fit as specified. The plot will also point to potential
-#'  outliers that may need further consideration.
+#' @description \code{linloess_plot()} provides a visual diagnostic of the
+#' linearity assumption of the OLS model. Provided an OLS 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
+#' fit as specified. The plot will also point to potential outliers that may
+#' need further consideration.
 #'
-#' @details This function makes an implicit assumption that there is no variable in the regression
-#'  formula with the name ".y".
+#' @details This function makes an implicit assumption that there is no variable
+#' in the regression formula with the name ".y" or ".resid".
 #'
 #' 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
-#' these to be fine the extent to which this is really just a visual aid and an informal diagnostic for the linearity assumption.
+#' @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 these to be fine the extent to which this is
+#' really just a visual aid and an informal diagnostic for the linearity
+#' assumption.
 #'
 #' @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.
+#' @param resid logical, defaults to \code{FALSE}. If \code{FALSE}, the y-axis
+#' on these plots are the raw values of the dependent variable. If \code{TRUE},
+#' the y-axis is the model's residuals. Either work well here for the matter
+#' at hand, provided you treat the output here as illustrative or suggestive.
+#' @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
 #'
@@ -32,22 +46,46 @@
 #' linloess_plot(M1, color="black", pch=21)
 
 
-linloess_plot <- function(mod, se = TRUE, ...) {
+linloess_plot <- function(mod, resid = FALSE, se = TRUE, ...) {
   modframe <- model.frame(mod)
 
-  dat <- gather(modframe, "var", "value", 2:ncol(modframe))
-  colnames(dat)[1] <- c(".y")
-
-  ggplot(dat, aes(.data$value, .data$.y)) +
-    # Create your facet now since the var variable is the particular x variable.
-    # Make sure to set scale="free_x" because these x variables are all on different scales
-    ggplot2::facet_wrap(~var, scale="free_x") +
-    # scatterplot
-    geom_point(...) +
-    # linear smoother
-    geom_smooth(method="lm", fill="blue", se = se) +
-    # loess smoother, with different color
-    geom_smooth(method="loess", color="black", linetype="dashed",
-                se = se)
+  if(resid == FALSE) {
+
+    dat <- gather(modframe, "var", "value", 2:ncol(modframe))
+    colnames(dat)[1] <- c(".y")
+
+    ggplot(dat, aes(.data$value, .data$.y)) +
+      # Create your facet now since the var variable is the particular x variable.
+      # Make sure to set scale="free_x" because these x variables are all on different scales
+      ggplot2::facet_wrap(~var, scale="free_x") +
+      # scatterplot
+      geom_point(...) +
+      # linear smoother
+      geom_smooth(method="lm", fill="blue", se = se) +
+      # loess smoother, with different color
+      geom_smooth(method="loess", color="black", linetype="dashed",
+                  se = se)
+
+  } else {
+    modframe$.resid <- resid(mod)
+    modframe <- modframe[c(".resid",names(modframe)[c(-1, -ncol(modframe))])]
+
+    dat <- gather(modframe, "var", "value", 2:ncol(modframe))
+    colnames(dat)[1] <- c(".resid")
+
+    ggplot(dat, aes(.data$value, .data$.resid)) +
+      # Create your facet now since the var variable is the particular x variable.
+      # Make sure to set scale="free_x" because these x variables are all on different scales
+      ggplot2::facet_wrap(~var, scale="free_x") +
+      # scatterplot
+      geom_point(...) +
+      # linear smoother
+      geom_smooth(method="lm", fill="blue", se = se) +
+      # loess smoother, with different color
+      geom_smooth(method="loess", color="black", linetype="dashed",
+                  se = se)
+  }
+
+
 
 }

R/p_z.R

@@ -18,7 +18,7 @@
 #'
 #' @param x a numeric vector (one or multiple) between 0 or 1
 #' @param ts a logical, defaults to TRUE. If TRUE, returns two-sided critical z-value.
-#'  If FALSE, the function returns a one-sized critical z-value.
+#'  If FALSE, the function returns a one-sided critical z-value.
 #'
 #' @return This function takes a numeric vector, corresponding to the p-value you want, and
 #' returns a numeric vector coinciding with the z-value you want under the standard normal

docs/pkgdown.yml

@@ -2,7 +2,7 @@ pandoc: 3.1.1
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-01-04T13:40Z
+last_built: 2024-01-11T09:34Z
 urls:
   reference: http://svmiller.com/reference
   article: http://svmiller.com/articles