evalMulticol: Evaluating multicollinearity using variance inflation factor

In jgodet/utilitR: Lots of useful functions

Evaluating multicollinearity using variance inflation factor

Description

Evaluating multicollinearity using variance inflation factor

Usage

evalMulticol(mod, threshold = 2)

Arguments

mod	a lm or a glm object

Details

calculation based on vif (package car) - threshold of 2 proposed in R in action R.I. Kabacoff (p.200)

Value

dataframe

Author(s)

JuG

References

1. Fox, J. and Monette, G. (1992) Generalized collinearity diagnostics. JASA, 87, 178–183.

2. Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Examples

data(mtcars)
mod <- lm(mpg~ .,data=mtcars)
evalMulticol(mod)

nIndep <- 6
mu<-runif(nIndep+1,2,7)
sigma<-rep(2, nIndep+1)
sample.size<-100
dataInd<-as.data.frame(mapply(function(x,y){rnorm(x,y,n=sample.size)},x=mu,y=sigma))
colnames(dataInd) <- c('y', paste("X",1:nIndep,sep=''))
mod2 <- lm(y~ ., data= dataInd)
evalMulticol(mod2)

jgodet/utilitR documentation built on April 13, 2025, 11:16 p.m.