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Chris Aberson January 30, 2026
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.
I built this under R 4.5.2
car (>= 3.0-0), stats (>= 3.5.0), dplyr (>= 0.8.0)
Please post issues using the link above (titled “isssues”). Those interested in contributing to further development should create a pull request.
This project is licensed under GNU General Public License version 3.
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
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
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
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
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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.