# cv.nnlasso.binomial: k-fold cross validation for penalized generalized linear... In nnlasso: Non-Negative Lasso and Elastic Net Penalized Generalized Linear Models

## Description

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

## Usage

 `1` ```cv.nnlasso.binomial(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)|β|_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.nnlasso function.

## Author(s)

Baidya Nath Mandal and Jun Ma

## References

Mandal, B.N. and Ma, J.(2016).L1 regularized multiplicative iterative path algorithm for non-negative generalized linear models

nnlasso documentation built on May 2, 2019, 8:19 a.m.