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("devtools") devtools::install_github("rsquaredacademy/olsrr")
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:
Regression
ols_regress(mpg ~ disp + hp + wt + qsec, data = mtcars) #> Model Summary #> -------------------------------------------------------------- #> R 0.914 RMSE 2.409 #> R-Squared 0.835 Coef. Var 13.051 #> Adj. R-Squared 0.811 MSE 6.875 #> Pred R-Squared 0.771 MAE 1.858 #> -------------------------------------------------------------- #> RMSE: Root Mean Square Error #> MSE: Mean Square Error #> MAE: Mean Absolute Error #> #> 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 #> ----------------------------------------------------------------------------------------
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.
Variable Selection
# stepwise regression model <- lm(y ~ ., data = surgical) ols_step_both_p(model) #> #> Stepwise Selection Summary #> ------------------------------------------------------------------------------------------ #> Added/ Adj. #> Step Variable Removed R-Square R-Square C(p) AIC RMSE #> ------------------------------------------------------------------------------------------ #> 1 liver_test addition 0.455 0.444 62.5120 771.8753 290.7604 #> 2 alc_heavy addition 0.567 0.550 41.3680 761.4394 259.1357 #> 3 enzyme_test addition 0.659 0.639 24.3380 750.5089 229.8956 #> 4 pindex addition 0.750 0.730 7.5370 735.7146 196.7872 #> 5 bcs addition 0.781 0.758 3.1920 730.6204 184.2762 #> ------------------------------------------------------------------------------------------
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.
Variable Selection
# stepwise aic backward regression model <- lm(y ~ ., data = surgical) k <- ols_step_backward_aic(model) k #> #> #> Backward Elimination Summary #> --------------------------------------------------------------------------- #> Variable AIC RSS Sum Sq R-Sq Adj. R-Sq #> --------------------------------------------------------------------------- #> Full Model 736.390 1825905.713 6543614.824 0.78184 0.74305 #> alc_mod 734.407 1826477.828 6543042.709 0.78177 0.74856 #> gender 732.494 1829435.617 6540084.920 0.78142 0.75351 #> age 730.620 1833716.447 6535804.090 0.78091 0.75808 #> ---------------------------------------------------------------------------
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 χ2 test.
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars) ols_test_breusch_pagan(model) #> #> Breusch Pagan Test for Heteroskedasticity #> ----------------------------------------- #> Ho: the variance is constant #> Ha: the variance is not constant #> #> Data #> ------------------------------- #> Response : mpg #> Variables: fitted values of mpg #> #> Test Summary #> --------------------------- #> DF = 1 #> Chi2 = 1.429672 #> Prob > Chi2 = 0.231818
Collinearity Diagnostics
model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars) ols_coll_diag(model) #> Tolerance and Variance Inflation Factor #> --------------------------------------- #> Variables Tolerance VIF #> 1 disp 0.1252279 7.985439 #> 2 hp 0.1935450 5.166758 #> 3 wt 0.1445726 6.916942 #> 4 qsec 0.3191708 3.133119 #> #> #> Eigenvalue and Condition Index #> ------------------------------ #> Eigenvalue Condition Index intercept disp hp wt #> 1 4.721487187 1.000000 0.000123237 0.001132468 0.001413094 0.0005253393 #> 2 0.216562203 4.669260 0.002617424 0.036811051 0.027751289 0.0002096014 #> 3 0.050416837 9.677242 0.001656551 0.120881424 0.392366164 0.0377028008 #> 4 0.010104757 21.616057 0.025805998 0.777260487 0.059594623 0.7017528428 #> 5 0.001429017 57.480524 0.969796790 0.063914571 0.518874831 0.2598094157 #> qsec #> 1 0.0001277169 #> 2 0.0046789491 #> 3 0.0001952599 #> 4 0.0024577686 #> 5 0.9925403056
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.
