Introduction to olsrr"

In olsrr: Tools for Building OLS Regression Models

Introduction

The olsrr package provides following tools for teaching and learning OLS regression using R:

• comprehensive regression output
• residual diagnostics
• measures of influence
• heteroskedasticity tests
• collinearity diagnostics
• model fit assessment
• variable contribution assessment
• variable selection procedures

library(olsrr)
library(ggplot2)
library(gridExtra)
library(nortest)
library(goftest)

This document is a quickstart guide to the tools offered by olsrr. Other vignettes provide more details on specific topics:

• Residual Diagnostics: Includes plots to examine residuals to validate OLS assumptions

• Variable selection: Differnt variable selection procedures such as all possible regression, best subset regression, stepwise regression, stepwise forward regression and stepwise backward regression

• Heteroskedasticity: Tests for heteroskedasticity include bartlett test, breusch pagan test, score test and f test

• Measures of influence: Includes 10 different plots to detect and identify influential observations

• Collinearity diagnostics: VIF, Tolerance and condition indices to detect collinearity and plots for assessing mode fit and contributions of variables

Regression

ols_regress(mpg ~ disp + hp + wt + qsec, data = mtcars)

In the presence of interaction terms in the model, the predictors are scaled and centered before computing the standardized betas. ols_regress() will detect interaction terms automatically but in case you have created a new variable instead of using the inline function *, you can indicate the presence of interaction terms by setting iterm to TRUE.

Residual vs Fitted Values Plot

Plot to detect non-linearity, unequal error variances, and outliers.

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

DFBETAs Panel

DFBETAs measure the difference in each parameter estimate with and without the influential observation. dfbetas_panel creates plots to detect influential observations using DFBETAs.

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

Residual Fit Spread Plot

Plot to detect non-linearity, influential observations and outliers.

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

Breusch Pagan Test

Breusch Pagan test is used to test for herteroskedasticity (non-constant error variance). It tests whether the variance of the errors from a regression is dependent on the values of the independent variables. It is a $\chi^{2}$ test.

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

Collinearity Diagnostics

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

Stepwise Regression

Build regression model from a set of candidate predictor variables by entering and removing predictors based on p values, in a stepwise manner until there is no variable left to enter or remove any more.

Variable Selection

# stepwise regression
model <- lm(y ~ ., data = surgical)
ols_step_both_p(model)

Plot

model <- lm(y ~ ., data = surgical)
k <- ols_step_both_p(model)
plot(k)

Stepwise AIC Backward Regression

Build regression model from a set of candidate predictor variables by removing predictors based on Akaike Information Criteria, in a stepwise manner until there is no variable left to remove any more.

Variable Selection

# stepwise aic backward regression
model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_aic(model)
k

Plot

model <- lm(y ~ ., data = surgical)
k <- ols_step_backward_aic(model)
plot(k)

olsrr documentation built on Feb. 10, 2020, 5:07 p.m.