Tools for Building OLS Regression Models.

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

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)
#>                          Model Summary                          
#> ---------------------------------------------------------------
#> R                       0.914       RMSE                 2.409 
#> R-Squared               0.835       MSE                  6.875 
#> Adj. R-Squared          0.811       Coef. Var           13.051 
#> Pred R-Squared          0.771       AIC                159.070 
#> MAE                     1.858       SBC                167.864 
#> ---------------------------------------------------------------
#>  RMSE: Root Mean Square Error 
#>  MSE: Mean Square Error 
#>  MAE: Mean Absolute Error 
#>  AIC: Akaike Information Criteria 
#>  SBC: Schwarz Bayesian Criteria 
#> 
#>                                ANOVA                                 
#> --------------------------------------------------------------------
#>                 Sum of                                              
#>                Squares        DF    Mean Square      F         Sig. 
#> --------------------------------------------------------------------
#> Regression     940.412         4        235.103    34.195    0.0000 
#> Residual       185.635        27          6.875                     
#> Total         1126.047        31                                    
#> --------------------------------------------------------------------
#> 
#>                                   Parameter Estimates                                    
#> ----------------------------------------------------------------------------------------
#>       model      Beta    Std. Error    Std. Beta      t        Sig      lower     upper 
#> ----------------------------------------------------------------------------------------
#> (Intercept)    27.330         8.639                  3.164    0.004     9.604    45.055 
#>        disp     0.003         0.011        0.055     0.248    0.806    -0.019     0.025 
#>          hp    -0.019         0.016       -0.212    -1.196    0.242    -0.051     0.013 
#>          wt    -4.609         1.266       -0.748    -3.641    0.001    -7.206    -2.012 
#>        qsec     0.544         0.466        0.161     1.166    0.254    -0.413     1.501 
#> ----------------------------------------------------------------------------------------

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.