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

In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty

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 ≤ 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=sample(c(0,1),100,replace=TRUE)
cv.binomial(x,y,k=5)

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