# plot4in1: Plot 4-in-1 In douglaswhitaker/MVQuickGraphs: Quick Multivariate Graphs

## Description

Generates a 2x2 panel graph including four residual diagnostic plots as is popular in some other statistics packages. This was initially written to support students learning R for the first time in a regression modeling course. `plot4in1` generates four commonly-used residual diagnostic plots that can be used to assess the linear regression assumptions and ensures a consistent, reasonably-pleasing graphical style across each plot.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```plot4in1( out, type = "Regular", PP = TRUE, pch = 19, col = "steelblue", cex = 1.2, ... ) ```

## Arguments

 `out` the output of the `lm` function (an object of class `"lm"`). The components of greatest importance from this object are `residuals` (perhaps passed to `rstandard` of `rstudent`, depending on `type`) and `fitted.values`. `type` the type of residuals to be used. There are three possible values: `"Regular"`, `"Standardized"`, and `"Studentized"`. Using `type = "Regular"` results in untransformed residuals being used, `type = "Standardized"` uses standardized residuals (computed using `rstandard`), and `type = "Studentized"` uses externally studentized residuals (computed using `rstudent`). `PP` logical. If `PP = TRUE`, a Normal Percentile Plot (P-P Plot) is displayed in the top-left panel. If `PP = FALSE`, a Normal Quantile Plot (Q-Q Plot) is displayed in the top-left panel. `pch` symbol to be used in plotting. `pch = 19` is a filled circle (see `par`). `col` color of symbol specified in `pch` to be used in graphing. The default is `"steelblue"` (see `par`). `cex` character expansion value, used to adjust the size of the symbol specified in `pch`. The default value is `cex = 1.2` (see `par`). `...` other arguments to be passed to the graphing functions.

## Details

`plot4in1` creates a 2 by 2 panel using `par(mfrow = c(2,2))` and then generates four residual diagnostic plots: a Percentile-Percentile (or Quantile-Quantile plot if `PP = FALSE`), a scatterplot of the `fitted.values` against the residuals, a histogram of the residuals, and scatterplot of the residuals against their order, overplotted.

## Value

None

`influence.measures` for more information about standardized (`rstandard`) and studentized (`rstudent`) residuals; `qqnorm` for more information about the Quantile-Quanitle (Q-Q) plot; `par` for information about the graphical parameters.
 ```1 2``` ```out <- lm(Girth ~ Volume, data = trees) plot4in1(out) ```