chemometrics (version 0.5)

ridgeCV: 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

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

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.

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)