README.md

gglm

Overview

gglm, The Grammar of Graphics for Linear Model Diagnostics, is a package that creates beautiful ggplot2 diagonostic plots for linear models that are easy to use and adhere to The Grammar of Graphics. The purpose of this package is to provide a sensible alternative to using the base-R plot() function to produce diagnostic plots for linear models.

Installation

# Currently, the best way to install is from GitHub.
devtools::install_github("graysonwhite/gglm")

Examples

gglm has two main types of functions. First, the gglm() function for quickly creating the four main diagnostic plots, similar to when you call plot() on an lm type object. Second, the stat_*() functions, which produce diagnostic plots the align with The Grammar of Graphics by creating ggplot2 layers that allow for easy plotting of particular model diagnostic plots.

Example 1: Quickly creating the four diagnostic plots with gglm()

library(gglm) # Load the package
data(mtcars) # Load example data
model <- lm(mpg ~ ., data = mtcars) # Create your model

gglm(model) # Plot the four main diagnostic plots

Example 2: Using the Grammar of Graphics with the stat_*() functions

library(ggplot2) # Need to load ggplot2

ggplot(data = model) +
  stat_fitted_resid()

# We can also add layers such as themes to these `ggplot`s and adjust features of the plot:
ggplot(data = model) +
  stat_cooks_leverage(alpha = 1) +
  theme_minimal()

Functions

For quick and easy plotting

gglm() plots the four default diagnostic plots when supplied an lm object. This function works similarly to plot.lm(), except that it displays the four diagnostic plots at once.

Following the Grammar of Graphics

stat_normal_qq(), stat_fitted_resid(), stat_resid_hist(), stat_scale_location(), stat_cooks_leverage(), stat_cooks_obs(), and stat_resid_leverage() all are ggplot2 layers used to create individual diagnostic plots. To use these, follow Example 2.



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gglm documentation built on Oct. 23, 2020, 7:57 p.m.