vif.lmridge {lmridge} | R Documentation |
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 class 'lmridge'
vif(x, ...)
Arguments
x |
For VIF method, an object of class "lmridge", i.e., a fitted model. |
... |
Not presently used in this implementation. |
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).
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.
Author(s)
Muhammad Imdad Ullah, Muhammad Aslam
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)