https://github.com/rsquaredacademy/olsrr

Tools for developing OLS regression models
https://github.com/rsquaredacademy/olsrr

collinearity-diagnostics linear-models regression rstats stepwise-regression

Last synced: about 17 hours ago
JSON representation

Tools for developing OLS regression models

• Host: GitHub
• URL: https://github.com/rsquaredacademy/olsrr
• Owner: rsquaredacademy
• License: other
• Created: 2016-11-28T17:58:45.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2024-07-19T12:32:08.000Z (7 months ago)
• Last Synced: 2024-10-31T16:12:08.444Z (4 months ago)
• Topics: collinearity-diagnostics, linear-models, regression, rstats, stepwise-regression
• Language: R
• Homepage: https://olsrr.rsquaredacademy.com/
• Size: 25.6 MB
• Stars: 102
• Watchers: 7
• Forks: 23
• Open Issues: 19
• Metadata Files:
  • Readme: README.Rmd
  • Changelog: NEWS.md
  • Contributing: CONTRIBUTING.MD
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md
  • Support: SUPPORT.md

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

```

# olsrr 

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/olsrr)](https://cran.r-project.org/package=olsrr)

[![R build status](https://github.com/rsquaredacademy/olsrr/workflows/R-CMD-check/badge.svg)](https://github.com/rsquaredacademy/olsrr/actions)

[![Coverage status](https://codecov.io/gh/rsquaredacademy/olsrr/branch/master/graph/badge.svg)](https://app.codecov.io/github/rsquaredacademy/olsrr?branch=master) 

## 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

```{r cran-installation, eval = FALSE}

# Install release version from CRAN

install.packages("olsrr")

# Install development version from GitHub

# install.packages("pak")

pak::pak("rsquaredacademy/olsrr")

```

## Articles

- [Quick Overview](https://olsrr.rsquaredacademy.com/articles/intro.html)

- [Variable Selection Methods](https://olsrr.rsquaredacademy.com/articles/variable_selection.html)

- [Residual Diagnostics](https://olsrr.rsquaredacademy.com/articles/residual_diagnostics.html)

- [Heteroskedasticity](https://olsrr.rsquaredacademy.com/articles/heteroskedasticity.html)

- [Measures of Influence](https://olsrr.rsquaredacademy.com/articles/influence_measures.html)

- [Collinearity Diagnostics](https://olsrr.rsquaredacademy.com/articles/regression_diagnostics.html)

## Usage

```{r, echo=FALSE, message=FALSE}

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

```{r regress}

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](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, 

use [StackOverflow](https://stackoverflow.com/).

## Code of Conduct

Please note that the olsrr project is released with a [Contributor Code of Conduct](https://olsrr.rsquaredacademy.com/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.