cv.nnlasso.normal: 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

cv.nnlasso.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)|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.