DMR4glm: Delete or Merge Regressors for Generalized Linear Models

In DMR: Delete or Merge Regressors for linear model selection.

Description

DMR4glm is a backward model selection procedure which simultaneously deletes continuous variables and merges levels of factors. It is a generalization of DMR onto generalized linear models, where instead of squared t-statistics, squared Wald statistics are used. The final model is selected by minimization of generalized information criterion in the nested family of models.

Usage

 DMR4glm(model, K = log(nrow(model$model)), clust.method = 'complete') 

Arguments

model	initial model of class glm.
K	penalty for the number of parameters in generalized information criterion, default is log(n).
clust.method	method of clustering, the same as in hclust.

Value

a list including elements

Partitions	a list of partitions of factors for the models on the nested path searched through
Crit	values of generalized information criterion for the models on the nested path searched through
LogLik	values of log-likelihood for the models on the nested path searched through
Models	a list of models of class glm on the nested path searched through
Best	a list containing features of the selected model: Partition, Model of class glm, Crit and Hypotheses represesnted as a matrix of lienear hypotheses imposed on the model's parameters

Author(s)

Piotr Pokarowski, Agnieszka Prochenka, Aleksandra Maj

See Also

DMR, stepDMR, plot_bf, roc

Examples

k <- 4
v1 <- factor(rep(1:8, each=12*k))
v2 <- factor(rep(1:4, times = 24*k))
v3 <- factor(rep(1:3, times = 32*k))
x1 <- rnorm(96*k)
x2 <- runif(96*k)
mi <- rep(c(2 , 2, -1, -1, -1, -1, 0, 0), each = 12*k)
y <- rbinom(96*k, 1, exp(mi)/(1+exp(mi)) )
m <- glm(y ~ x1 + x2 + v1 + v2 + v3, family = binomial)
(out <- DMR4glm(m))

DMR documentation built on May 30, 2017, 6:25 a.m.