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

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

Adjusted R-squared in Normal Linear Models

Description

Extracts the adjusted R-squared in normal linear models.

Usage

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

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.

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,

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)

glmtoolbox documentation built on Sept. 11, 2024, 7:32 p.m.