BetterReg Readme

Chris Aberson January 30, 2026

BetterReg

This package provides tools for statistics that are not provided in base R packages for linear regression and logistic regression. Functions provide squared semi-partial correlations, tolerance, Mahalanobis Distances, Likelihood Ratio Chi-square, and Pseudo R-square.

Prerequisites

I built this under R 4.5.2

Authors

• Chris Aberson chrisaberson

Dependencies

car (>= 3.0-0), stats (>= 3.5.0), dplyr (>= 0.8.0)

Issues and Contributions

Please post issues using the link above (titled "isssues"). Those interested in contributing to further development should create a pull request.

License

This project is licensed under GNU General Public License version 3.

Examples

part function for squared semipartial correlations

The part function requires an existing LM model and indication of number of predictors.

library(BetterReg)
mymodel<-lm(y~x1+x2+x3+x4+x5, data=testreg)
parts(model=mymodel, pred=5)

Predictor 1: semi partial = 0.032; squared semipartial = 0.001
Predictor 2: semi partial = 0.307; squared semipartial = 0.094
Predictor 3: semi partial = 0.268; squared semipartial = 0.072
Predictor 4: semi partial = 0.134; squared semipartial = 0.018
Predictor 5: semi partial = 0.241; squared semipartial = 0.058

tolerance function for multicollinearity assumptions

The tolerance function requires only a model.

mymodel<-lm(y~x1+x2+x3+x4+x5, data=testreg)
tolerance(model=mymodel)

    x1        x2        x3        x4        x5 

0.9976977 0.9990479 0.9931082 0.9953317 0.9980628

Mahal function for detecting multivariate outliers

The Mahal function requires model, predictors, and desired number of values to output.

mymodel<-lm(y~x1+x2+x3+x4+x5, data=testreg)
Mahal(model=mymodel, pred=5, values=10)

  537      770      342      760      299      982      446      174 

14.56342 15.03188 15.56224 15.60986 16.52869 16.80958 17.38597 18.11072 458 530 20.02762 25.09934

LRchi function for Logistic Regression Coefficients

The LRchi function takes input for the dependent variable name (y), up to 10 predictors (x1, x2, etc.), and the number of predictors.

LRchi(data=testlog, y="dv", x1="iv1", x2="iv2",numpred=2)

Predictor: iv1; LR squared 34.09, p= 0
Predictor: iv2; LR squared 0.19, p= 0.67

Pseudo function for Logistic Regression Effect Size

The Psuedo function takes an existing model as input

mymodel<-glm(dv~iv1+iv2+iv3+iv4, testlog,family = binomial())
pseudo(model=mymodel)

Likelihood Ratio R-squared (McFadden, Recommended) = 0.26
Cox-Snell R-squared) = 0.301
Nagelkerk R-squared = 0.402