lmCV {chemometrics} | R Documentation

## 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

### 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

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

[Package chemometrics version 0.6 Index]