## olsrr: Tools for building OLS Regression models

Author: Aravind Hebbali
License: MIT

## Overview

The olsrr package provides following tools for teaching and learning OLS regression using R:

• Comprehensive Regression Output
• Variable Selection Procedures
• Heteroskedasticity Tests
• Collinearity Diagnostics
• Model Fit Assessment
• Measures of Influence
• Residual Diagnostics
• Variable Contribution Assessment

## Installation

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")``````

## Shiny App

Use `ols_launch_app()` to explore the package using a shiny app.

## Consistent Prefix

olsrr uses consistent prefix `ols_` for easy tab completion.

## Quick Demo

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
#> ----------------------------------------------------------------------------------------``````
##### 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_bp_test(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
#> ---------------------------------------
#> # A tibble: 4 x 3
#>   Variables Tolerance      VIF
#>       <chr>     <dbl>    <dbl>
#> 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``````
##### 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_stepwise(model)
#> We are selecting variables based on p value...
#> 1 variable(s) added....
#> 1 variable(s) added...
#> 1 variable(s) added...
#> 1 variable(s) added...
#> 1 variable(s) added...
#> No more variables to be added or removed.
#> Stepwise Selection Method
#>
#> Candidate Terms:
#>
#> 1 . bcs
#> 2 . pindex
#> 3 . enzyme_test
#> 4 . liver_test
#> 5 . age
#> 6 . gender
#> 7 . alc_mod
#> 8 . alc_heavy
#>
#> ------------------------------------------------------------------------------------------
#>                                 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    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)
ols_stepaic_backward(model)
#>
#>
#>                        Backward Elimination Summary
#> -------------------------------------------------------------------------
#> Variable        AIC          RSS          Sum Sq       R-Sq     Adj. R-Sq
#> -------------------------------------------------------------------------
#> Full Model    736.390    1825905.713    6543614.824    0.782        0.743
#> alc_mod       734.407    1826477.828    6543042.709    0.782        0.749
#> gender        732.494    1829435.617    6540084.920    0.781        0.754
#> age           730.620    1833716.447    6535804.090    0.781        0.758
#> -------------------------------------------------------------------------``````

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.