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# 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("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                2.622
#> 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
#> ------------------------------------------------------------------------------------------
#> Step     Variable      Removed     R-Square    R-Square     C(p)        AIC         RMSE
#> ------------------------------------------------------------------------------------------
#>    1    liver_test     addition       0.455       0.444    62.5120    771.8753    296.2992
#>    2     alc_heavy     addition       0.567       0.550    41.3680    761.4394    266.6484
#>    3    enzyme_test    addition       0.659       0.639    24.3380    750.5089    238.9145
#>    4      pindex       addition       0.750       0.730     7.5370    735.7146    206.5835
#>    5        bcs        addition       0.781       0.758     3.1920    730.6204    195.4544
#> ------------------------------------------------------------------------------------------

#### 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 (\chi^{2}) 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
#> 1 4.721487187        1.000000 0.000123237 0.001132468 0.001413094
#> 2 0.216562203        4.669260 0.002617424 0.036811051 0.027751289
#> 3 0.050416837        9.677242 0.001656551 0.120881424 0.392366164
#> 4 0.010104757       21.616057 0.025805998 0.777260487 0.059594623
#> 5 0.001429017       57.480524 0.969796790 0.063914571 0.518874831
#>             wt         qsec
#> 1 0.0005253393 0.0001277169
#> 2 0.0002096014 0.0046789491
#> 3 0.0377028008 0.0001952599
#> 4 0.7017528428 0.0024577686
#> 5 0.2598094157 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.

#### Install

install.packages('olsrr')

8,874

#### Version

0.5.3

MIT + file LICENSE

#### Last Published

February 10th, 2020