DMR (version 2.0)

DMR4glm: Delete or Merge Regressors for Generalized Linear Models

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

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))