Diagnostic Plots"
In rmcorr: Repeated Measures Correlation

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

knitr::opts_chunk$set(echo = TRUE, tidy = FALSE)
options(width = 80)
library(knitr)
library(rmarkdown)
library(rmcorr) 
library(gglm)

Running Example Requires gglm [@gglm]

#Install gglm
install.packages("gglm")
require(gglm)

Plotting Model Diagnostics

The code below demonstrates how to plot model diagnostics for rmcorr. There are four diagnostic plots assessing:
1. Residuals vs. Fitted values: Linearity
2. Quantile-Quantile (Q-Q): Normality of residuals
3. Scale-Location: Equality of variance (homoscedasticity)
4. Residuals vs. Leverage: Influential observations

raz.rmc <- rmcorr(participant = Participant, measure1 = Age, 
                  measure2 = Volume, dataset = raz2005) 

#Using gglm
 gglm(raz.rmc$model)

#using base R 
#plot(raz.rmc$model)

How much do violations of these assumptions matter? It depends. General Linear Model (GLM) is typically robust to deviations from the above assumptions, but severe violations may produce misleading results [@gelman2020regression]. Also, the reason(s) for violations can matter: "Violations of assumptions may result from problems in the dataset, the use of an incorrect regression model, or both" [@cohen2013applied, p. 117].