# cv.enetLTS

##### Cross-validation for the `enetLTS`

object

Does k-fold cross-validation for enetLTS, produces a plot,
and returns optimal values for `alpha`

and `lambda`

.

- Keywords
- models, regression

##### Usage

`cv.enetLTS(index=NULL,xx,yy,family,h,alphas,lambdas,nfold,repl,ncores,plot=TRUE)`

##### Arguments

- index
A user supplied index. The default is

`NULL`

.- xx
matrix

`xx`

as in`enetLTS`

.- yy
response

`yy`

as in`enetLTS`

.- family
a description of the error distribution and link function to be used in the model.

`"gaussian"`

and`"binomial"`

options are available.- h
a user supplied numeric value giving how many observations will be used.

- alphas
a user supplied alpha sequence for the elastic net penalty, which is the mixing proportion of the ridge and lasso penalties and takes value in [0,1]. Here \(\alpha=1\) is the lasso penalty, and \(\alpha=0\) the ridge penalty.

- lambdas
a user supplied lambda sequence for the strength of the elastic net penalty.

- nfold
a user supplied numeric value for fold number of k-fold cross-validation which used in varied functions of the algorithm. The default is 5-fold cross-validation.

- repl
a user supplied posiitive number for more stable results, repeat the k-fold CV

`repl`

times and take the average of the corresponding evaluation measure. The default is 5.- ncores
a positive integer giving the number of processor cores to be used for parallel computing. The default is 4.

- plot
a logical indicating if produces a plot for k-fold cross-validation based on alpha and lambda combinations. The default is TRUE.

##### Value

produces a plot,
and returns optimal values for `alpha`

and `lambda`

##### Note

This is an internal function. But, it is also available for direct usage to obtain optimal values of alpha and lambda for user supplied index set.

*Documentation reproduced from package enetLTS, version 0.1.0, License: GPL (>= 3)*