genridge (version 0.6-2)

vif.ridge: Variance Inflation Factors for Ridge Regression

Description

The function vif.ridge calculates variance inflation factors for the predictors in a set of ridge regression models indexed by the tuning/shrinkage factor.

Usage

## S3 method for class 'ridge':
vif(mod, ...)

Value

Returns a matrix of variance inflation factors of the same size and shape as coef{mod}. The columns correspond to the predictors in the model and the rows correspond to the values of lambda in ridge estimation.

Details

Variance inflation factors are calculated using the simplified formulation in Fox & Monette (1992).

References

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

See Also

vif, ~~~

Examples

data(longley)
lmod <- lm(Employed ~ GNP + Unemployed + Armed.Forces + Population + Year + GNP.deflator, data=longley)
vif(lmod)

longley.y <- longley[, "Employed"]
longley.X <- data.matrix(longley[, c(2:6,1)])

lambda <- c(0, 0.005, 0.01, 0.02, 0.04, 0.08)
lridge <- ridge(longley.y, longley.X, lambda=lambda)
coef(lridge)


vridge <- vif(lridge)
vridge

# plot VIFs
pch <- c(15:18, 7, 9)
clr <- c("black", rainbow(5, start=.6, end=.1))

matplot(rownames(vridge), vridge, type='b', 
	xlab='Ridge constant (k)', ylab="Variance Inflation", 
	xlim=c(0, 0.08), 
	col=clr, pch=pch, cex=1.2)
text(0.0, vridge[1,], colnames(vridge), pos=4)

matplot(lridge$df, vridge, type='b', 
	xlab='Degrees of freedom', ylab="Variance Inflation", 
	col=clr, pch=pch, cex=1.2)
text(6, vridge[1,], colnames(vridge), pos=2)