lmCV: Repeated Cross Validation for lm

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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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, Boca Raton, FL, 2009.

See Also

mvr

Examples

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


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