chemometrics (version 0.5)

plotRidge: Plot results of Ridge regression

Description

Two plots from Ridge regression are generated: The MSE resulting from Generalized Cross Validation (GCV) versus the Ridge parameter lambda, and the regression coefficients versus lambda. The optimal choice for lambda is indicated.

Usage

plotRidge(formula, data, lambda = seq(0.5, 50, by = 0.05), ...)

Arguments

formula

formula, like y~X, i.e., dependent~response variables

data

data frame to be analyzed

lambda

possible values for the Ridge parameter to evaluate

...

additional plot arguments

Value

• predicted
  predicted values for the optimal lambda
• lambdaopt
  optimal Ridge parameter lambda from GCV

Details

For all values provided in lambda the results for Ridge regression are computed. The function lm.ridge is used for cross-validation and Ridge regression.

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

lm.ridge, plotRidge

Examples

data(PAC)
res=plotRidge(y~X,data=PAC,lambda=seq(1,20,by=0.5))