AICc_BIC_glmnetB: AICc and BIC for glmnet logistic models

In SelectBoost: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets

AICc and BIC for glmnet logistic models

Description

Compute AICc and BIC for glmnet logistic models.

Usage

rerr(v1, v2)

ridge_logistic(X, Y, lambda, beta0, beta, maxiter = 1000, tol = 1e-10)

BIC_glmnetB(Z, Y, glmnet.model, alpha, modelSet, reducer = "median")

AICc_glmnetB(Z, Y, glmnet.model, alpha, modelSet, reducer = "median")

Arguments

v1	A numeric vector.
v2	A numeric vector.
X	A numeric matrix
Y	A numeric 0/1 vector.
lambda	A numeric value.
beta0	A numeric value Initial intercept value.
beta	A numeric vector. Initial coefficient values.
maxiter	A numeric value. Maximum number of iterations.
tol	A numeric value. Tolerance value.
Z	A numeric matrix
glmnet.model	A fitted glmnet model.
alpha	A numeric value.
modelSet	Modelset to consider.
reducer	A character value. Reducer function. Either 'median' or 'mean'.

Details

Calculate AICc and BIC for glmnet logistic models from the glmnetB function of the package rLogistic https://github.com/echi/rLogistic and adapted to deal with non finite exponential values in AICc and BIC computations

Value

A list relevant to model selection.

Author(s)

Frederic Bertrand, [e-mail]

References

Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion, Chi and Scott, Journal of Computational and Graphical Statistics, 23(1), 2014, p111–128, doi: 10.1080/10618600.2012.737296.

See Also

var_select

Examples

set.seed(314)
xran=matrix(rnorm(150),30,5)
ybin=sample(0:1,30,replace=TRUE)
glmnet.fit <- glmnet.fit <- glmnet::glmnet(xran,ybin,family="binomial",standardize=FALSE)
set.seed(314)
rerr(1:10,10:1)

set.seed(314)
ridge_logistic(xran,ybin,lambda=.5,beta0=rnorm(5),beta=rnorm(5,1))

set.seed(314)
if(is.factor(ybin)){ynum=unclass(ybin)-1} else {ynum=ybin}
subSample <- 1:min(ncol(xran),100)
BIC_glmnetB(xran,ynum,glmnet.fit,alpha=1,subSample, reducer='median')

set.seed(314)
if(is.factor(ybin)){ynum=unclass(ybin)-1} else {ynum=ybin}
subSample <- 1:min(ncol(xran),100)
AICc_glmnetB(xran,ynum,glmnet.fit,alpha=1,subSample, reducer='median')

SelectBoost documentation built on Dec. 1, 2022, 1:27 a.m.