The olsrr package provides following tools for teaching and learning OLS regression using R:
-
Comprehensive Regression Output
-
Variable Selection Procedures
- All Possible Regression
- Best Subsets Regression
- Stepwise Regression
- Stepwise Forward Regression
- Stepwise Backward Regression
- stepAIC Regression
- stepAIC Forward Regression
- stepAIC Backward Regression
-
Heteroskedasticity Tests
- Bartlett Test
- Breusch Pagan Test
- F Test
- Score Test
-
Collinearity Diagnostics
- Variance Inflation Factors
- Tolerance
- Eigenvalues
- Condition Indices
-
Model Fit Assessment
- Residual Fit Spread Plot
- Part & Partial Correlations
- Observed vs Fitted Values Plot
- Lack of Fit F Test
- Diagnostics Panel
-
Measures of Influence
- Cook's D Bar Plot
- Cook's D Chart
- DFBETAs Panel
- DFFITS Plot
- Studentized Residual Plot
- Standardized Residual Chart
- Studentized Residuals vs Leverage Plot
- Deleted Studentized Residuals vs Fitted Values Plot
- Hadi Plot
- Potential Residual Plot
-
Residual Diagnostics
- Residual QQ Plot
- Residual Histogram
- Residual Box Plot
- Residual Normality Test
- Residual vs Fitted Values Plot
-
Variable Contribution Assessment
- Residual vs Regressor Plot
- Added Variable Plot
- Residual Plus Component Plot
You can install olsrr from github with:
# install olsrr from CRAN
install.packages("olsrr")
# the development version from github
# install.packages("devtools")
devtools::install_github("rsquaredacademy/olsrr")
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.