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)
#> Warning in rmcorr(participant = Participant, measure1 = Age, measure2 = Volume,
#> : 'Participant' coerced into a factor
#Using gglm
gglm(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 (Gelman, Hill, and Vehtari 2020). 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” (Cohen et al. 2013, 117).

Cohen, Jacob, Patricia Cohen, Stephen G West, and Leona S Aiken. 2013.
*Applied Multiple Regression/Correlation Analysis for the Behavioral
Sciences*. Routledge.

Gelman, Andrew, Jennifer Hill, and Aki Vehtari. 2020. *Regression and
Other Stories*. Cambridge University Press.

White, Grayson. 2023. *Gglm: Grammar of Graphics for Linear Model
Diagnostic Plots*. https://CRAN.R-project.org/package=gglm.