ridgregextra
focuses on finding the ridge parameter
value k which makes the VIF values closest to 1 while keeping them above
1 as stressed “Applied Linear Statistical Models” (Kutner et al., 2004).
The package includes the ridgereg_k
function, presents a
system that automatically determines the k value in a certain range
defined by the user and provides detailed ridge regression results.
ridgereg_k
also provides ridge regression tables (VIF, MSE,
R2, Beta, Stdbeta) using vif_k
function for k ridge
parameter values generated between certain lower and upper bound
values.
In addition, the ridge_reg
function provides users the
ridge regression results for a manually entered k value. Finally
ridgregextra
provides three sets of graphs consisting k
versus VIF values, regression coefficents and standard errors of
them.
ridgregextra
was presented for the first time in “Why R?
Turkey 2022” conference.
ridgregextra
Please make sure that you installed devtools
package.
devtools::install_github(filizkrdg/ridgregextra)
You can use isdals
package to have example data to test
ridgregextra
package. Please make sure that you installed
the package.
library(isdals)
data(bodyfat)
x=bodyfat[,-1]
y=bodyfat[,1]
ridgereg_k
function to get coefficients by using
alternative approach to traditional ridge regression techniques.ridgereg_k(x,y,0,1)
You can use mctest
package to have example data to test
ridgregextra
package. Please make sure that you installed
the package.
library("mctest")
x=Hald[,-1]
y=Hald[,1]
ridgereg_k(x,y,0,1)
For any questions please contact: