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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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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