# olsrr

## 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("pak")
``````

## Usage

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:

#### Regression

``````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
#> ----------------------------------------------------------------------------------------
``````

## 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 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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olsrr documentation built on May 29, 2024, 12:35 p.m.