In rsquaredacademy/olsrr: Tools for Building OLS Regression Models

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olsrr

Overview

The olsrr package provides following tools for building OLS regression models using R:

• Comprehensive Regression Output
• Variable Selection Procedures
• Heteroskedasticity Tests
• Collinearity Diagnostics
• Model Fit Assessment
• Measures of Influence
• Residual Diagnostics
• Variable Contribution Assessment

Installation

# Install release version from CRAN
install.packages("olsrr")

# Install development version from GitHub
# install.packages("pak")
pak::pak("rsquaredacademy/olsrr")

Articles

• Quick Overview
• Variable Selection Methods
• Residual Diagnostics
• Heteroskedasticity
• Measures of Influence
• Collinearity Diagnostics

Usage

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

olsrr uses consistent prefix ols_ for easy tab completion. If you know how to write a formula or build models using lm, you will find olsrr very useful. Most of the functions use an object of class lm as input. So you just need to build a model using lm and then pass it onto the functions in olsrr. Below is a quick demo:

Regression

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

Getting Help

If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.

Code of Conduct

Please note that the olsrr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

rsquaredacademy/olsrr documentation built on Nov. 8, 2024, 5:01 a.m.