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

 cv.extlasso R Documentation

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

### Description

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

### Usage

```cv.extlasso(x,y,family=c("binomial","normal","poisson"),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. `family` family is either "normal" or "binomial" or "poisson". `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/ deviances 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 uses prediction means squared errors for normal family and deviance for binomial and poisson family.

### 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

```#normal family
x=matrix(rnorm(100*30),100,30)
y=rnorm(100)
cv.extlasso(x,y,family="normal",k=5)

#binomial family
x=matrix(rnorm(100*30),100,30)
y=sample(c(0,1),100,replace=TRUE)
cv.extlasso(x,y,family="binomial",k=5)

#poisson family
x=matrix(rnorm(100*30),100,30)
y=sample(c(1:5),100,replace=TRUE)
cv.extlasso(x,y,family="poisson",k=5)
```

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