chemometrics: lmCV – R documentation

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lmCV

Repeated Cross Validation for lm

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

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

`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

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

`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

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

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

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

Multivariate Statistical Analysis in Chemometrics

v1.4.2

GPL (>= 3)

Authors

Peter Filzmoser and Kurt Varmuza

Initial release

2017-03-17