R: CV for Lasso regression

lassoCV {chemometrics} | R Documentation

## CV for Lasso regression

### Description

Performs cross-validation (CV) for Lasso regression and plots the results in order to select the optimal Lasso parameter.

### Usage

    lassoCV(formula, data, K = 10, fraction = seq(0, 1, by = 0.05), trace = FALSE,
    plot.opt = TRUE, sdfact = 2, legpos = "topright", ...)

### Arguments

`formula` | formula, like y~X, i.e., dependent~response variables
`data` | data frame to be analyzed
`K` | the number of segments to use for CV
`fraction` | fraction for Lasso parameters to be used for evaluation, see details
`trace` | if 'TRUE', intermediate results are printed
`plot.opt` | if TRUE a plot will be generated that shows optimal choice for "fraction"
`sdfact` | factor for the standard error for selection of the optimal parameter, see details
`legpos` | position of the legend in the plot
`...` | additional plot arguments

### Details

The parameter "fraction" is the sum of absolute values of the regression coefficients for a particular Lasso parameter on the sum of absolute values of the regression coefficients for the maximal possible value of the Lasso parameter (unconstrained case), see also `lars`. The optimal fraction is chosen according to the following criterion: Within the CV scheme, the mean of the SEPs is computed, as well as their standard errors. Then one searches for the minimum of the mean SEPs and adds sdfact*standarderror. The optimal fraction is the smallest fraction with an MSEP below this bound.

### Value

`cv` | MSEP values at each value of fraction
`cv.error` | standard errors for each value of fraction
`SEP` | SEP value for each value of fraction
`ind` | index of fraction with optimal choice for fraction
`sopt` | optimal value for fraction
`fraction` | all values considered for fraction

### 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

`cv.lars`, `lassocoef`

### Examples

    data(PAC)
    # takes some time: # res <- lassoCV(y~X,data=PAC,K=5,fraction=seq(0.1,0.5,by=0.1))

[Package chemometrics version 1.4.2 Index]