plotRidge {chemometrics} | R Documentation

## 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.

### See Also

`lm.ridge`, `plotRidge`

### Examples

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

[Package chemometrics version 0.6 Index]