The olsrr package provides following tools for building OLS regression models using R:
# Install release version from CRAN
install.packages("olsrr")
# Install development version from GitHub
# install.packages("pak")
pak::pak("rsquaredacademy/olsrr")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:
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
#> ----------------------------------------------------------------------------------------If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.
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.