library(olsrr)
library(dplyr)
library(ggplot2)
library(gridExtra)
library(purrr)
library(tibble)
library(nortest)
library(goftest)

Introduction

olsrr offers tools for detecting violation of standard regression assumptions. Here we take a look at residual diagnostics. The standard regression assumptions include the following about residuals/errors:

Residual QQ Plot

Graph for detecting violation of normality assumption.

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_plot_resid_qq(model)

Residual Normality Test

Test for detecting violation of normality assumption.

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_normality(model)

Correlation between observed residuals and expected residuals under normality.

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_test_correlation(model)

Residual vs Fitted Values Plot

It is a scatter plot of residuals on the y axis and fitted values on the x axis to detect non-linearity, unequal error variances, and outliers.

Characteristics of a well behaved residual vs fitted plot:

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_plot_resid_fit(model)

Residual Histogram

Histogram of residuals for detecting violation of normality assumption.

model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
ols_plot_resid_hist(model)


cmlopera/olsrr documentation built on May 26, 2019, 10:34 a.m.