R: Repeated CV for Ridge regression

ridgeCV {chemometrics} | R Documentation

## Repeated CV for Ridge regression

### Description

Performs repeated cross-validation (CV) to evaluate the result of Ridge regression where the optimal Ridge parameter lambda was chosen on a fast evaluation scheme.

### Usage

    ridgeCV(formula, data, lambdaopt, repl = 5, segments = 10,
       segment.type = c("random", "consecutive", "interleaved"), length.seg,
       trace = FALSE, plot.opt = TRUE, ...)

### Arguments

`formula` | formula, like y~X, i.e., dependent~response variables
`data` | data frame to be analyzed
`lambdaopt` | optimal Ridge parameter lambda
`repl` | number of replications for the CV
`segments` | the number of segments to use for CV, or a list with segments (see `mvrCv`)
`segment.type` | the type of segments to use. Ignored if 'segments' is a list
`length.seg` | Positive integer. The length of the segments to use. If specified, it overrides 'segments' unless 'segments' is a list
`trace` | logical; if 'TRUE', the segment number is printed for each segment
`plot.opt` | if TRUE a plot will be generated that shows the predicted versus the observed y-values
`...` | additional plot arguments

### Details

Generalized Cross Validation (GCV) is used by the function `lm.ridge` to get a quick answer for the optimal Ridge parameter. This function should make a careful evaluation once the optimal parameter lambda has been selected. Measures for the prediction quality are computed and optionally plots are shown.

### Value

`residuals` | matrix of size length(y) x repl with residuals
`predicted` | matrix of size length(y) x repl with predicted values
`SEP` | Standard Error of Prediction computed for each column of "residuals"
`SEPm` | mean SEP value
`sMAD` | MAD of Prediction computed for each column of "residuals"
`sMADm` | mean of MAD values
`RMSEP` | Root MSEP value computed for each column of "residuals"
`RMSEPm` | mean RMSEP value

### 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=ridgeCV(y~X,data=PAC,lambdaopt=4.3,repl=5,segments=5)

[Package chemometrics version 1.4.2 Index]