knitr::opts_chunk$set( echo = TRUE, warning = FALSE, message = FALSE, fig.path = "man/figures/readme-" )
ggResidpanel is an R package for creating panels of diagnostic plots for a model using ggplot2 and interactive versions of the plots using plotly.
The code below shows how ggResidpanel can be installed from CRAN. If desired, the development version of ggResidpanel can be installed from GitHub.
# Installs ggResidpanel from CRAN install.packages("ggResidpanel") # Installs the development version of ggResidpanel from GitHub devtools::install_github("goodekat/ggResidpanel")
Load the ggResidpanel package.
# Load the library library(ggResidpanel)
Here are some resources for learning how to use ggResidpanel:
The package provides five functions that allow the user to assess diagnostic plots from a model. These functions are:
resid_panel
: Creates a panel of diagnostic plots of the residuals from a modelresid_interact
: Creates an interactive panel of diagnostic plots of the residuals form a modelresid_xpanel
: Creates a panel of diagnostic plots of the predictor variables resid_compare
: Creates a panel of diagnostic plots from multiple modelsresid_auxpanel
: Creates a panel of diagnostic plots for model types not included in the packageCurrently, ggResidpanel allows the first four functions listed above to work with models fit using the functions of lm
, glm
, lme
(from nlme), and lmer
or glmer
(from lme4 or fit using lmerTest). Each of these functions is applied below to show the panel that is output from the function. The functions have multiple input options such as the formatting options of scale
, theme
, axis.text.size
, title.text.size
, and title.opt
.
The penguins
data used in the examples below is included in ggResidpanel.
str(penguins)
resid_panel
This function creates a panel of residual diagnostic plots given a model. It allows the user to select a panel of plots from the options in the package or create their own panel by selecting from the plots available for this function.
# Fit a model penguin_model <- lme4::lmer(heartrate ~ depth + duration + (1|bird), data = penguins) # Create the default panel of plots resid_panel(penguin_model) # Create a pancel with residual, qq, and yvp plots, add 95% confidence interval # bands to the qq-plot, and change the theme to classic resid_panel(penguin_model, plots = c("resid", "qq", "yvp"), qqbands = TRUE, theme = "classic") # Create a panel with all plots available for a model fit using lmer resid_panel(penguin_model, plots = "all")
resid_interact
This function creates interactive versions of residual diagnostic plot panels given a model. Similar to resid_panel
, it allows the user to select a panel of plots from the options in the package or to create their own panel by selecting from the plots available for this function.
# Create an interactive panel of the default diagnostic plots resid_interact(penguin_model)
resid_xpanel
This function creates a panel of plots of the residuals or response variable versus the predictor (x) variables in the model.
# Create a panel of plots of the residuals versus the predictor variables resid_xpanel(penguin_model, jitter.width = 0.1) # Create a panel of plots of the response variable versus the predictor variables resid_xpanel(penguin_model, yvar = "response", jitter.width = 0.1)
resid_compare
This function creates a panel of residual diagnostic plots given a list of models. This allows the user to compare the diagnostic plots from multiple models.
# Fit the model with a log transformation of the response variable and a # quadratic term for duration penguin_model_log2 <- lme4::lmer(log(heartrate) ~ depth + duration + I(duration^2) + (1|bird), data = penguins) # Plot the residual and normal quantile plots for the two models resid_compare(list(penguin_model, penguin_model_log2), plots = c("resid", "qq"))
resid_auxpanel
This function creates a panel of residual diagnostic plots given inputs of residuals and fitted values to use for models not accepted by resid_panel
. Users can select from panel options in the package or create their own panel from the plots available for this function.
# Fit a regression tree to the penguins data penguin_tree <- rpart::rpart(heartrate ~ depth + duration, data = penguins) # Obtain the predictions from the model on the observed data penguin_tree_pred <- predict(penguin_tree) # Obtain the residuals from the model penguin_tree_resid <- penguins$heartrate - penguin_tree_pred # Create a panel with the residual and index plot resid_auxpanel(residuals = penguin_tree_resid, predicted = penguin_tree_pred, plots = c("resid", "index"))
# copy static figures to docs folder file.copy("./README_files/", "./docs", overwrite = TRUE, recursive = TRUE)
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