lmCV {chemometrics}R Documentation

Repeated Cross Validation for lm

Description

Repeated Cross Validation for multiple linear regression: a cross-validation is performed repeadedly, and standard evaluation measured are returned.

Usage

lmCV(formula, data, repl = 100, segments = 4, segment.type = c("random", "consecutive", "interleaved"), length.seg, trace = FALSE, ...)

Arguments

formula formula, like y~X, i.e., dependent~response variables
data data set including y and X
repl number of replication for Cross Validation
segments number of segments used for splitting into training and test data
segment.type "random", "consecutive", "interleaved" splitting into training and test data
length.seg number of parts for training and test data, overwrites segments
trace if TRUE intermediate results are reported
... additional plotting arguments

Details

Repeating the cross-validation with allow for a more careful evaluation.

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
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. To appear.

See Also

mvr

Examples

data(ash)
set.seed(100)
res=lmCV(SOT~.,data=ash,repl=10)
hist(res$SEP)

[Package chemometrics version 0.4 Index]