extlasso (version 0.3)

cv.binomial: k-fold cross validation for penalized generalized linear models for binomial family

Description

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

Usage

cv.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, \(0 \le \tau \le 1\) in elastic net penalty \(\lambda\{\tau\|\beta\|_1+(1-\tau)\|beta\|_2^2\}\). 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

References

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

Examples

# NOT RUN {
x=matrix(rnorm(100*30),100,30)
y=sample(c(0,1),100,replace=TRUE)
cv.binomial(x,y,k=5)
# }