adjR2.glm: Adjusted R-squared in Generalized Linear Models

In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

Adjusted R-squared in Generalized Linear Models

Description

Computes the adjusted deviance-based R-squared in generalized linear models.

Usage

## S3 method for class 'glm'
adjR2(..., digits = 4, verbose = TRUE)

Arguments

...	one or several objects of the class glm, which are obtained from the fit of generalized linear models.
digits	an (optional) integer value indicating the number of decimal places to be used. By default, digits is set to be 4.
verbose	an (optional) logical indicating if should the report of results be printed. By default, verbose is set to be TRUE.

Details

The deviance-based R-squared is computed as R^2=1 - Deviance/Null.Deviance. Then, the adjusted deviance-based R-squared is computed as 1 - \frac{n-1}{n-p}(1-R^2), where p is the number of parameters in the linear predictor and n is the sample size.

Value

a matrix with the following columns

Deviance	value of the residual deviance,
R-squared	value of the deviance-based R-squared,
df	number of parameters in the linear predictor,
adj.R-squared	value of the adjusted deviance-based R-squared,

Examples

###### Example 1: Fuel efficiency of cars
Auto <- ISLR::Auto
fit1 <- glm(mpg ~ horsepower*weight, family=Gamma(inverse), data=Auto)
fit2 <- update(fit1, formula=mpg ~ horsepower*weight*cylinders)
fit3 <- update(fit1, family=Gamma(log))
fit4 <- update(fit2, family=Gamma(log))
fit5 <- update(fit1, family=inverse.gaussian(log))
fit6 <- update(fit2, family=inverse.gaussian(log))

AIC(fit1,fit2,fit3,fit4,fit5,fit6)
BIC(fit1,fit2,fit3,fit4,fit5,fit6)
adjR2(fit1,fit2,fit3,fit4,fit5,fit6)

glmtoolbox documentation built on Oct. 10, 2023, 9:06 a.m.