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 5.801 #> 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.
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