---
title: "Diagnostic Plots"
author: "Jonathan Bakdash and Laura Marusich"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
bibliography: references.bib 
vignette: >
  %\VignetteIndexEntry{Diagnostic Plots}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy = FALSE)
options(width = 80)
library(knitr)
library(rmarkdown)
library(rmcorr) 
library(gglm) 

```

#### Running Example Requires gglm [@gglm]
```{r, eval = FALSE}
#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:
<br>
1. Residuals vs. Fitted values: Linearity
<br>
2. Quantile-Quantile (Q-Q): Normality of residuals
<br>
3. Scale-Location: Equality of variance (homoscedasticity)
<br>
4. Residuals vs. Leverage: Influential observations

```{r}
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].