chemometrics: plotRidge – R documentation

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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

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.

Value

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

Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

References

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

Examples

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

chemometrics

Multivariate Statistical Analysis in Chemometrics

v1.4.2

GPL (>= 3)

Authors

Peter Filzmoser and Kurt Varmuza

Initial release

2017-03-17