#' Panel of Diagnostic Residual Plots Across Multiple Models.
#'
#' Creates a panel of residual diagnostic plots given a list of models. Currently accepts
#' models of type "lm", "glm", "lmerMod", "lmerModLmerTest", and "glmerMod".
#'
#' @param models List of models fit using either \code{lm}, \code{glm}, \code{lmer},
#' \code{lmerTest}, or \code{glmer}.
#' @param plots Plots chosen to include in the panel of plots. The default panel
#' includes a residual plot, a normal quantile plot, an index plot,
#' and a histogram of the residuals. (See details for the options available.)
#' @param type Type of residuals to use in the plot. If not specified, the
#' default residual type for each model type is used. (See details for the
#' options available.)
#' @param bins Number of bins to use when creating a histogram of the residuals.
#' Default is set to 30.
#' @param smoother Indicates whether or not to include a smoother on the index,
#' residual-leverage, location-scale, and residual plots. Specify TRUE or FALSE.
#' Default is set to FALSE.
#' @param qqline Indicates whether to include a 1-1 line on the qq-plot. Specify
#' TRUE or FALSE. Default is set to TRUE.
#' @param qqbands Indicates whether to include confidence bands on the qq-plot.
#' Specify TRUE or FALSE. Default is set to FALSE.
#' @param scale Scales the size of the graphs in the panel. Takes values in (0,1].
#' @param theme ggplot2 theme to be used. Current options are \code{"bw"},
#' \code{"classic"}, and \code{"grey"} (or \code{"gray"}). Default is
#' \code{"bw"}.
#' @param axis.text.size Specifies the size of the text for the axis labels of
#' all plots in the panel.
#' @param title.text.size Specifies the size of the text for the titles of all
#' plots in the panel.
#' @param title.opt Indicates whether or not to include a title on the plots in
#' the panel. Specify TRUE or FALSE. Default is set to TRUE.
#' @param nrow Sets the number of rows in the panel.
#'
#' @export resid_compare
#'
#' @details
#'
#' The first two sections below contain information on the available input
#' options for the \code{plots} and \code{type} arguments in \code{resid_compare}.
#' The third section contains details relating to the creation of the plots.
#'
#' \strong{Options for Plots}
#'
#' The following options can be chosen for the \code{plots} argument.
#' \itemize{
#' \item "all": This creates a panel of all plot types included in the package
#' that are available for the model type input into \code{residpanel}. (See note
#' below.)
#' \item "default": This creates a panel with a residual plot, a normal quantile plot
#' of the residuals, an index plot of the residuals, and a histogram of the residuals.
#' \item "R": This creates a panel with a residual plot, a normal
#' quantile plot of the residuals, a location-scale plot, and a leverage versus
#' residuals plot. This was modeled after the plots shown in R if the
#' \code{plot()} base function is applied to an \code{lm} model. This option can
#' only be used with an \code{lm} or \code{glm} model.
#' \item "SAS": This creates a panel with a residual plot, a normal quantile plot of
#' the residuals, a histogram of the residuals, and a boxplot of the residuals.
#' This was modeled after the residpanel option in proc mixed from SAS version 9.4.
#' \item A vector of individual plots can also be specified.
#' For example, one can specify \code{plots = c("boxplot", "hist")} or
#' \code{plots = "qq"}. The individual plot options are as follows.
#' \itemize{
#' \item \code{"boxplot"}: A boxplot of residuals
#' \item \code{"cookd"}: A plot of Cook's D values versus observation numbers
#' \item \code{"hist"}: A histogram of residuals
#' \item \code{"index"}: A plot of residuals versus observation numbers
#' \item \code{"ls"}: A location scale plot of the residuals
#' \item \code{"qq"}: A normal quantile plot of residuals
#' \item \code{"lev"}: A plot of leverage values versus residuals
#' \item \code{"resid"}: A plot of residuals versus predicted values
#' \item \code{"yvp":}: A plot of observed response values versus predicted values
#' } }
#'
#' Note: \code{"cookd"}, \code{"ls"}, and \code{"lev"} are only available for "lm"
#' and "glm" models.
#'
#' \strong{Options for Type}
#'
#' Several residual types are available to be requested based on the model type
#' that is input into \code{resid_panel}. These currently are as follows.
#' \itemize{
#' \item \code{lm} residual options
#' \itemize{
#' \item \code{"pearson"}:The Pearson residuals
#' \item \code{"response"}: The raw residuals (Default for "lm")
#' \item \code{"standardized"}: The standardized raw residuals
#' }
#' \item \code{glm} residual options
#' \itemize{
#' \item \code{"pearson"}: The Pearson residuals
#' \item \code{"deviance"}: The deviance residuals (Default for "glm")
#' \item \code{"response"}: The raw residuals
#' \item \code{"stand.deviance"}: The standardized deviance residuals
#' \item \code{"stand.pearson"}: The standardized Pearson residuals
#' }
#' \item \code{lmer}, \code{lmerTest}, and \code{lme} residual options
#' \itemize{
#' \item \code{"pearson"}: The Pearson residuals (Default for "lmer", "lmerTest", and "lme")
#' \item \code{"response"}: The raw residuals
#' }
#' \item \code{glmer} residual options
#' \itemize{
#' \item \code{"pearson"}: The Pearson residuals
#' \item \code{"deviance"}: The deviance residuals (Default for "glmer")
#' \item \code{"response"}: The raw residuals
#' } }
#'
#' Note: The plots of \code{"ls"} and \code{"lev"} only accept standardized residuals.
#'
#' \strong{Details on the Creation of Plots}
#'
#' \describe{
#' \item{Boxplot (\code{boxplot})}{Boxplot of the residuals.}
#'
#' \item{Cook's D (\code{cookd})}{ The horizontal line represents a cut-off to identify
#' highly influential points. The horizontal line is placed at 4/n where n is
#' the number of data points used in the \code{model}.}
#'
#' \item{Histogram (\code{hist})}{Plots a histogram of the residuals. The density
#' curve overlaid has mean equal to zero and standard deviation equal to the
#' standard deviation of the residuals.}
#'
#' \item{Index Plot (\code{index})}{Plots the residuals on the y-axis and the observation
#' number associated with the residual on the x-axis.}
#'
#' \item{Leverage Plot (\code{lev})}{Plots the standardized residuals on the y-axis
#' and the leverage values on the x-axis. A lowess curve is overlaid, and Cook's
#' D contours are included for \eqn{\alpha = 0.5} and \eqn{\alpha = 1}.}
#'
#' \item{Location-Scale Plot (\code{ls})}{Plots the square root of the absolute value
#' of the standardized residuals on the y-axis and the predicted values on the
#' x-axis. The predicted values are plotted on the original scale for \code{glm}
#' and \code{glmer} models. A lowess curve is overlaid.}
#'
#' \item{QQ Plot (\code{qq})}{Makes use of the \code{R} package \code{qqplotr} for
#' creating a normal quantile plot of the residuals.}
#'
#' \item{Residual Plot (\code{resid})}{Plots the residuals on the y-axis and the
#' predicted values on the x-axis. The predicted values are plotted on the
#' original scale for \code{glm} and \code{glmer} models.}
#'
#' \item{Response vs. Predicted (\code{yvp})}{Plots the response variable from the
#' model on the y-axis and the predicted values on the x-axis. Both response
#' variable and predicted values are plotted on the original scale for
#' \code{glm} and \code{glmer} models.}
#'}
#'
#' @return A panel of residual diagnostic plots containing plots specified for each model.
#'
#' @examples
#'
#' # Fit two models to the penguins data
#' penguin_model <- lme4::lmer(heartrate ~ depth + duration + (1|bird), data = penguins)
#' penguin_model_log2 <- lme4::lmer(log(heartrate) ~ depth + duration + I(duration^2) +
#' (1|bird), data = penguins)
#'
#' # Compare the residuals from the model
#' resid_compare(list(penguin_model, penguin_model_log2))
#'
#' # Adjust some options in the panel of plots
#' resid_compare(list(penguin_model, penguin_model_log2), plots = c("resid", "yvp"),
#' smoother = TRUE, theme = "grey")
resid_compare <-
function(models,
plots = "default",
type = NA,
bins = 30,
smoother = FALSE,
qqline = TRUE,
qqbands = FALSE,
scale = 1,
theme = "bw",
axis.text.size = 10,
title.text.size = 12,
title.opt = TRUE,
nrow = NULL) {
## Set number of rows
compare_rows <- length(plots)
if (compare_rows == 1) {
if (plots == "all") {
compare_rows <- 9
} else if (plots %in% c("default", "R", "SAS")) {
compare_rows <- 4
}
}
## Errors and Warnings -------------------------------------------------------
# Checks that return an error
for (i in 1:length(models)) {
check_modeltype(model = models[[i]])
check_residualtype(model = models[[i]], type = type[i])
check_standardized(model = models[[i]], plots = plots)
check_cooksd(model = models[[i]], plots = plots)
check_leverage(model = models[[i]], plots = plots)
}
# Checks that return a warning
smoother <- check_smoother(smoother = smoother)
theme <- check_theme(theme = theme)
title.opt <- check_title(title.opt = title.opt)
## Creation of plots ---------------------------------------------------------
# Create a boxplot of the residuals if selected in plots otherwise set as NULL
if ("boxplot" %in% plots | "SAS" %in% plots | "all" %in% plots) {
boxplot_list <- list()
for (i in 1:length(models)) {
boxplot_list[[i]] <- plot_boxplot(
type = type,
model = models[[1]],
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else{
boxplot_list <- NULL
}
# Create a Cook's D plot if selected in plots otherwise set as NULL
if ("cookd" %in% plots) {
cookd_list <- list()
for (i in 1:length(models)) {
cookd_list[[i]] <- plot_cookd(
model = models[[i]],
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else if ("all" %in% plots) {
check_count <- 0
for (i in 1:length(models)) {
if (!(class(models[[i]])[1] %in% c("lme", "lmerMod", "lmerModLmerTest", "glmerMod"))) {
check_count <- check_count + 1
}
}
if (check_count == length(models)) {
cookd_list <- list()
for (i in 1:length(models)) {
cookd_list[[i]] <- plot_cookd(
model = models[[i]],
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
}
} else{
cookd_list <- NULL
}
# Create a histogram of the residuals if selected in plots otherwise set as NULL
if ("hist" %in% plots |
"default" %in% plots | "SAS" %in% plots | "all" %in% plots) {
hist_list <- list()
for (i in 1:length(models)) {
hist_list[[i]] <- plot_hist(
model = models[[i]],
type = type,
bins = bins,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else{
hist_list <- NULL
}
# Create an index plot of the residuals if selected in plots otherwise set as NULL
if ("index" %in% plots | "default" %in% plots | "all" %in% plots) {
index_list <- list()
for (i in 1:length(models)) {
index_list[[i]] <- plot_index(
model = models[[i]],
type = type,
theme = theme,
smoother = smoother,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else{
index_list <- NULL
}
# Create a residual-leverage plot if selected in plots otherwise set as NULL
if ("lev" %in% plots | "R" %in% plots) {
lev_list <- list()
for (i in 1:length(models)) {
lev_list[[i]] <- plot_lev(
model = models[[i]],
type = type,
smoother = smoother,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else if ("all" %in% plots) {
check_count <- 0
for (i in 1:length(models)) {
if (!(class(models[[i]])[1] %in% c("lme", "lmerMod", "lmerModLmerTest", "glmerMod"))) {
check_count <- check_count + 1
}
}
if (check_count == length(models)) {
lev_list <- list()
for (i in 1:length(models)) {
lev_list[[i]] <- plot_lev(
model = models[[i]],
type = type,
smoother = smoother,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
}
} else{
lev_list <- NULL
}
# Create a location-scale plot if selected in plots otherwise set as NULL
if ("ls" %in% plots | "R" %in% plots) {
ls_list <- list()
for (i in 1:length(models)) {
ls_list[[i]] <- plot_ls(
model = models[[i]],
type = type,
smoother = smoother,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else if ("all" %in% plots) {
check_count <- 0
for (i in 1:length(models)) {
if (!(class(models[[i]])[1] %in% c("lme", "lmerMod", "lmerModLmerTest", "glmerMod"))) {
check_count <- check_count + 1
}
}
if (check_count == length(models)) {
ls_list <- list()
for (i in 1:length(models)) {
ls_list[[i]] <- plot_ls(
model = models[[i]],
type = type,
smoother = smoother,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
}
} else{
ls_list <- NULL
}
# Create a q-q plot of the residuals if selected in plots otherwise set as NULL
if ("qq" %in% plots |
"default" %in% plots |
"SAS" %in% plots | "R" %in% plots | "all" %in% plots) {
qq_list <- list()
for (i in 1:length(models)) {
qq_list[[i]] <- plot_qq(
model = models[[i]],
type = type,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt,
qqline = qqline,
qqbands = qqbands
)
}
} else{
qq_list <- NULL
}
# Create a residual plot if selected in plots otherwise set as NULL
if ("resid" %in% plots |
"default" %in% plots |
"SAS" %in% plots | "R" %in% plots | "all" %in% plots) {
resid_list <- list()
for (i in 1:length(models)) {
resid_list[[i]] <- plot_resid(
model = models[[i]],
type = type,
smoother = smoother,
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else{
resid_list <- NULL
}
# Create a plot of the response variable vs the predicted values if selected
# in plots otherwise set as NULL
if ("yvp" %in% plots | "all" %in% plots) {
yvp_list <- list()
for (i in 1:length(models)) {
yvp_list[[i]] <- plot_yvp(
model = models[[i]],
theme = theme,
axis.text.size = axis.text.size,
title.text.size = title.text.size,
title.opt = title.opt
)
}
} else{
yvp_list <- NULL
}
## Creation of grid of plots -------------------------------------------------
# If individual plots have been specified, set plots equal to "individual"
# Return an error if none of the correct plot options have been specified
if ("default" %in% plots |
"SAS" %in% plots | "R" %in% plots | "all" %in% plots) {
plots <- plots
} else if ("boxplot" %in% plots |
"cookd" %in% plots | "index" %in% plots |
"hist" %in% plots | "ls" %in% plots | "qq" %in% plots |
"lev" %in% plots |
"resid" %in% plots | "yvp" %in% plots) {
chosen <- plots
plots <- "individual"
} else{
stop("Invalid plots option entered. See the resid_panel help file for
available options.")
}
# Create a grid of plots based on the plots specified
if (plots == "default") {
# Create grid of the default plots
suppressWarnings(plot_grid(
plotlist = c(resid_list, qq_list, index_list, hist_list),
scale = scale,
nrow = compare_rows
))
} else if (plots == "SAS") {
# Create grid of the default plots
suppressWarnings(plot_grid(
plotlist = c(resid_list, hist_list, qq_list, boxplot_list),
scale = scale,
nrow = compare_rows
))
} else if (plots == "R") {
# Create grid of the default plots
suppressWarnings(plot_grid(
plotlist = c(resid_list, qq_list, ls_list, lev_list),
scale = scale,
nrow = compare_rows
))
} else if (plots == "all") {
# Create grid of all plots
if (class(models)[1] == "lm" | class(models)[1] == "glm") {
# Create grid of the default plots
suppressWarnings(plot_grid(
plotlist = c(
resid_list,
index_list,
yvp_list,
qq_list,
hist_list,
boxplot_list,
cookd_list,
ls_list,
lev_list
),
scale = scale,
nrow = compare_rows
))
} else{
# Create grid of the default plots
suppressWarnings(plot_grid(
plotlist = c(
resid_list,
index_list,
yvp_list,
qq_list,
hist_list,
boxplot_list
),
scale = scale,
nrow = compare_rows
))
}
} else if (plots == "individual") {
# Turn the specified plots into a list
individual_plots <- list(
boxplot = boxplot_list,
cookd = cookd_list,
hist = hist_list,
index = index_list,
ls = ls_list,
qq = qq_list,
lev = lev_list,
resid = resid_list,
yvp = yvp_list
)
# Select the chosen plots
individual_plots <- individual_plots[chosen]
# Creates a sublevel list so need to turn into a list with only one level
individual_plots_final <- list()
index <- 1
for (i in 1:length(chosen)) {
for (j in 1:length(models)) {
individual_plots_final[[index]] <- individual_plots[[i]][[j]]
index <- index + 1
}
}
# Create grid of the default plots
suppressWarnings(plot_grid(
plotlist = individual_plots_final,
scale = scale,
nrow = compare_rows
))
}
}
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