plot_redres: Diagnostic residual plot for linear mixed models

In goodekat/redres: Residuals and Diagnostic Plots for Mixed Models

Description

Creates a plot of residuals versus fitted values or model variable. This plot can be used to assess whether the assumptions of constant variance and linear form assumptions are adequate.

Usage

plot_redres(model, type = "raw_cond", xvar = NULL)

Arguments

model	Model fit using lmer from lme4.
type	String identifying type of residual. Default is "raw_cond". See compute_redres for details of available types.
xvar	String indicating the variable to be plotted on the x-axis. By default, the fitted values are plotted on the x-axis. Any variable used in the lmer model can be specified.

Value

A residual plot in the form of a ggplot2 object.

Examples

# fits a linear mixed effects model
library(lme4)
fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)

# plots raw conditional residuals by fitted values.
plot_redres(fm1)

# plots raw conditional residuals by selected variables `Days`.
plot_redres(fm1, xvar = "Days")

# plots standardized conditional residuals by fitted values.
plot_redres(fm1, type = "std_cond")

# edits theme of ggplot2 object
library(ggplot2)
plot_redres(fm1, type = "pearson_mar") + ggtitle("Pearson Marginal Residuals by Fitted Values")

goodekat/redres documentation built on March 3, 2021, 10:15 a.m.