lmridge (version 1.2.2)

vif.lmridge: Variance Inflation Fator for Linear Ridge Regression

Description

Computes VIF values for each scalar or vector value of biasing parameter \(K\) (Marquardt, 1970).

Usage

vif(x, ...)
# S3 method for lmridge
vif(x, ...)

Value

The vif function returns a matrix of VIF values for each regressor after adding scalar or vector biasing parameter \(K\) to \(X'X\) matrix. The column of returned matrix indicates regressors name and row indicates value of each biasing parameter \(K\) provided as argument to lmridge function.

Arguments

x

For VIF method, an object of class "lmridge", i.e., a fitted model.

...

Not presently used in this implementation.

Author

Muhammad Imdad Ullah, Muhammad Aslam

Details

The vif.lmridge function computes VIF value for each regressor in data set after addition of biasing parameter as argument to lmridge function. The VIF is computed using \((X'X+kI)^{-1}X'X(X'X+kI)^{-1}\), given by Marquardt, (1970).

References

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

Imdad, M. U. Addressing Linear Regression Models with Correlated Regressors: Some Package Development in R (Doctoral Thesis, Department of Statistics, Bahauddin Zakariya University, Multan, Pakistan), 2017.

Marquardt, D. (1970). Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation. Technometrics, 12(3), 591--612.

See Also

The ridge model fitting lmridge, ridge Var-Cov matrix vcov

Examples

data(Hald)
mod <- lmridge(y~., data = as.data.frame(Hald), scaling = "sc", K = seq(0,1,.2) )
vif(mod)