lassoCV: CV for Lasso regression In chemometrics: Multivariate Statistical Analysis in Chemometrics

Description

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

Usage

 ```1 2``` ```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.

`cv.lars`, `lassocoef`

Examples

 ```1 2``` ```data(PAC) # takes some time: # res <- lassoCV(y~X,data=PAC,K=5,fraction=seq(0.1,0.5,by=0.1)) ```

Example output

```Loading required package: rpart
```

chemometrics documentation built on May 1, 2019, 7:58 p.m.