chemometrics (version 1.4.2)

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), ...)

Value

predicted

predicted values for the optimal lambda

lambdaopt

optimal Ridge parameter lambda from GCV

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

Author

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

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

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

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