# cv.normal: k-fold cross validation for penalized generalized linear... In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty

 cv.normal R Documentation

## k-fold cross validation for penalized generalized linear models for normal family

### Description

The function does k-fold cross validation for selecting best value of regularization parameter.

### Usage

cv.normal(x,y,k=5,nlambda=50,tau=1,plot=TRUE,errorbars=TRUE)

### Arguments

 x x is matrix of order n x p where n is number of observations and p is number of predictor variables. Rows should represent observations and columns should represent predictor variables. y y is a vector of response variable of order n x 1. k Number of folds for cross validation. Default is k=5. nlambda Number of lambda values to be used for cross validation. Default is nlambda=50. tau Elastic net parameter, 0 ≤ τ ≤ 1 in elastic net penalty λ\{τ\|β\|_1+(1-τ)\|beta\|_2^2\}. Default tau=1 corresponds to LASSO penalty. plot if TRUE, produces a plot of cross validated prediction mean squared errors against lambda. Default is TRUE. errorbars If TRUE, error bars are drawn in the plot. Default is TRUE.

### Value

Produces a plot and returns a list with following components:

 lambda Value of lambda for which average cross validation error is minimum pmse A vector of average cross validation errors for various lambda values lambdas A vector of lambda values used in cross validation se A vector containing standard errors of cross validation errors

### Note

This function need not be called by user. The function is internally called by cv.extlasso function.

### Author(s)

B N Mandal and Jun Ma

### References

Mandal, B.N. and Jun Ma, (2014). A Jacobi-Armijo Algorithm for LASSO and its Extensions.

### Examples

x=matrix(rnorm(100*30),100,30)
y=rnorm(100)
cv.normal(x,y,k=10)

extlasso documentation built on May 13, 2022, 9:08 a.m.