chemometrics (version 0.5)

lmCV: 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

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

Details

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

References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

See Also

mvr

Examples

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