# Tools for Building OLS Regression Models

Tools designed to make it easier for users, particularly beginner/intermediate R users to build ordinary least squares regression models. Includes comprehensive regression output, heteroskedasticity tests, collinearity diagnostics, residual diagnostics, measures of influence, model fit assessment and variable selection procedures.

## 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

You can install olsrr from github with:

## Shiny App

Use ols_launch_app() to explore the package using a shiny app.

## Usage

olsrr uses consistent prefix ols_ for easy tab completion.

olsrr is built with the aim of helping those users who are new to the R language. 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:

#### 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.

#### 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.

#### 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.

## 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 this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

# olsrr 0.5.2

This is a minor release to fix bugs from breaking changes in recipes package and other enhancements.

## Enhancements

• variable selection procedures now return the final model as an object of class lm (#81)
• data preparation functions of selected plots are now exported to enable end users to create customized plots and to use plotting library of their choice (#86)

# olsrr 0.5.1

This is a patch release to fix minor bugs and improve error messages.

## Enhancements

olsrr now throws better error messages keeping in mind beginner and intermediate R users. It is a work in progress and should get better in future releases.

## Bug Fixes

Variable selection procedures based on p values now handle categorical variables in the same way as the procedures based on AIC values.

# olsrr 0.5.0

This is a minor release for bug fixes and API changes.

## API Changes

We have made some changes to the API to make it more user friendly:

• all the variable selection procedures start with ols_step_*
• all the test start with ols_test_*
• all the plots start with ols_plot_*

## Bug Fixes

• ols_regress returns error in the presence of interaction terms in the formula (#49)

• ols_regress returns error in the presence of interaction terms in the formula (#47)

• return current version (#48)

# olsrr 0.4.0

## Enhancements

• use ols_launch_app() to launch a shiny app for building models
• save beta coefficients for each independent variable in ols_all_subset() (#41)

## Bug Fixes

• mismatch in sign of partial and semi partial correlations (#44)
• error in diagnostic panel (#45)
• standardized betas in the presence of interaction terms (#46)

A big thanks goes to (Dr. Kimberly Henry) for identifying bugs and other valuable feedback that helped improve the package.

# olsrr 0.3.0

This is a minor release containing bug fixes.

## Bug Fixes

• output from reg_compute rounded up to 3 decimal points (#24)
• added variable plot fails when model includes categorical variables (#25)
• all possible regression fails when model includes categorical predictors (#26)
• output from bartlett test rounded to 3 decimal points (#27)
• best subsets regression fails when model includes categorical predictors (#28)
• output from breusch pagan test rounded to 4 decimal points (#29)
• output from collinearity diagnostics rounded to 3 decimal points (#30)
• cook's d bar plot threshold rounded to 3 decimal points (#31)
• cook's d chart threshold rounded to 3 decimal points (#32)
• output from f test rounded to 3 decimal points (#33)
• output from measures of influence rounded to 4 decimal points (#34)
• output from information criteria rounded to 4 decimal points (#35)
• studentized residuals vs leverage plot threshold rounded to 3 decimal points (#36)
• output from score test rounded to 3 decimal points (#37)
• step AIC backward method AIC value rounded to 3 decimal points (#38)
• step AIC backward method AIC value rounded to 3 decimal points (#39)
• step AIC both direction method AIC value rounded to 3 decimal points (#40)

# olsrr 0.2.0

This is a minor release containing bug fixes and minor improvements.

## Bug Fixes

• inline functions in model formula caused errors in stepwise regression (#2)
• added variable plots (ols_avplots) returns error when model formula contains inline functions (#3)
• all possible regression (ols_all_subset) returns an error when the model formula contains inline functions or interaction variables (#4)
• best subset regression (ols_best_subset) returns an error when the model formula contains inline functions or interaction variables (#5)
• studentized residual plot (ols_srsd_plot) returns an error when the model formula contains inline functions (#6)
• stepwise backward regression (ols_step_backward) returns an error when the model formula contains inline functions or interaction variables (#7)
• stepwise forward regression (ols_step_backward) returns an error when the model formula contains inline functions (#8)
• stepAIC backward regression (ols_stepaic_backward) returns an error when the model formula contains inline functions (#9)
• stepAIC forward regression (ols_stepaic_forward) returns an error when the model formula contains inline functions (#10)
• stepAIC regression (ols_stepaic_both) returns an error when the model formula contains inline functions (#11)
• outliers incorrectly plotted in (ols_cooksd_barplot) cook's d bar plot (#12)
• regression (ols_regress) returns an error when the model formula contains inline functions (#21)
• output from step AIC backward regression (ols_stepaic_backward) is not properly formatted (#22)
• output from step AIC regression (ols_stepaic_both) is not properly formatted (#23)

## Enhancements

• cook's d bar plot (ols_cooksd_barplot) returns the threshold value used to classify the observations as outliers (#13)
• cook's d chart (ols_cooksd_chart) returns the threshold value used to classify the observations as outliers (#14)
• DFFITs plot (ols_dffits_plot) returns the threshold value used to classify the observations as outliers (#15)
• deleted studentized residuals vs fitted values plot (ols_dsrvsp_plot) returns the threshold value used to classify the observations as outliers (#16)
• studentized residuals vs leverage plot (ols_rsdlev_plot) returns the threshold value used to detect outliers/high leverage observations (#17)
• standarized residuals chart (ols_srsd_chart) returns the threshold value used to classify the observations as outliers (#18)
• studentized residuals plot (ols_srsd_plot) returns the threshold value used to classify the observations as outliers (#19)

## Documentation

There were errors in the description of the values returned by some functions. The documentation has been thoroughly revised and improved in this release.

First release.

# Reference manual

install.packages("olsrr")

0.5.2 by Aravind Hebbali, 6 months ago

Browse source code at https://github.com/cran/olsrr

Authors: Aravind Hebbali [aut, cre]

Documentation:   PDF Manual