Introduction

As an R programmer, you may want to create added variable plots to visualize the relationship between a predictor variable and the response variable while controlling for the effects of other predictor variables. In this blog post, we will use the car library and the avPlots() function to create added variable plots in R.

What are Added Variable Plots?

Added variable plots, also known as partial-regression plots, are used to visualize the relationship between a predictor variable and the response variable while controlling for the effects of other predictor variables. They are useful for identifying non-linear relationships and outliers, and for checking the linearity assumption of a linear regression model.

How to Create Added Variable Plots in R

To create added variable plots in R, we will use the avPlots() function from the car library. Here is an example code block:

# Load the car library
library(car)

Loading required package: carData

# Fit a linear regression model
model <- lm(mpg ~ disp + hp + drat + am, data = mtcars)

# Create added variable plots
avPlots(model)

Let’s break down this code block:

• First, we load the car library using the library() function.
• Next, we fit a linear regression model using the lm() function. The model formula specifies mpg as the response variable and disp, hp, am and drat as the predictor variables. The data for the model is mtcars.
• Finally, we create added variable plots using the avPlots() function and passing in the model object.

Resources

• [1] https://rdrr.io/cran/car/man/avPlots.html
• [2] https://search.r-project.org/CRAN/refmans/car/html/avPlots.html
• [3] https://www.geeksforgeeks.org/how-to-create-added-variable-plots-in-r/
• [4] https://stackoverflow.com/questions/59150905/is-there-a-ggplot2-analogue-to-the-avplots-function-in-r
• [6] https://www.rdocumentation.org/link/avPlots?package=car&version=2.0-0