lmCV: Repeated Cross Validation for lm

In chemometrics: Multivariate Statistical Analysis in Chemometrics

Repeated Cross Validation for lm

Description

Repeated Cross Validation for multiple linear regression: a cross-validation is performed repeatedly, and standard evaluation measures 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 <[e-mail]>

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)

chemometrics documentation built on Aug. 25, 2023, 5:18 p.m.