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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, ...)
matrix of size length(y) x repl with residuals
matrix of size length(y) x repl with predicted values
Standard Error of Prediction computed for each column of "residuals"
mean SEP value
Root MSEP value computed for each column of "residuals"
mean RMSEP value
formula, like y~X, i.e., dependent~response variables
data set including y and X
number of replication for Cross Validation
number of segments used for splitting into training and test data
"random", "consecutive", "interleaved" splitting into training and test data
number of parts for training and test data, overwrites segments
if TRUE intermediate results are reported
additional plotting arguments
Peter Filzmoser <P.Filzmoser@tuwien.ac.at>
Repeating the cross-validation with allow for a more careful evaluation.
K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.
mvr
data(ash) set.seed(100) res=lmCV(SOT~.,data=ash,repl=10) hist(res$SEP)
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