glmtoolbox (version 0.1.12)

adjR2.lm: Adjusted R-squared in Normal Linear Models

Description

Extracts the adjusted R-squared in normal linear models.

Usage

# S3 method for lm
adjR2(..., digits = max(3, getOption("digits") - 2), verbose = TRUE)

Value

a matrix with the following columns

RSS	value of the residual sum of squares,
R-squared	value of the R-squared,
df	number of parameters in the linear predictor,
adj.R-squared	value of the adjusted R-squared,

Arguments

...

one or several objects of the class lm, which are obtained from the fit of normal linear models.

digits

an (optional) integer value indicating the number of decimal places to be used. As default, digits is set to max(3, getOption("digits") - 2).

verbose

an (optional) logical indicating if should the report of results be printed. As default, verbose is set to TRUE.

Details

The R-squared is computed as \(R^2=1 - RSS/Null.RSS\). Then, the adjusted 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.

Examples

###### Example 1: Fuel efficiency of cars
fit1 <- lm(mpg ~ log(hp) + log(wt) + qsec, data=mtcars)
fit2 <- lm(mpg ~ log(hp) + log(wt) + qsec + log(hp)*log(wt), data=mtcars)
fit3 <- lm(mpg ~ log(hp)*log(wt)*qsec, data=mtcars)

AIC(fit1,fit2,fit3)
BIC(fit1,fit2,fit3)
adjR2(fit1,fit2,fit3)