msBIC: PHM model selection with BIC

Description

This function fits models for different proportionality restrictions.

Usage

msBIC(x, K, method = "all", Sdist = "weibull", cutpoint = NULL, 
EMoption = "classification", EMstop = 0.01, maxiter = 100)

Arguments

Data frame or matrix of dimension n*p with survival times (NA's allowed).

A vector with number of mixture components.

method

A vector with the methods provided in phmclust: With "separate" no restrictions are imposed, "main.g" relates to a group main effect, "main.p" to the variables main effects. "main.gp" reflects the proportionality assumption over groups and variables. "int.gp" allows for interactions between groups and variables. If method is "all", each model is fitted.

Sdist

Various survival distrubtions such as "weibull", "exponential", and "rayleigh".

cutpoint

Cutpoint for censoring

EMoption

"classification" is based on deterministic cluster assignment, "maximization" on deterministic assignment, and "randomization" provides a posterior-based randomized cluster assignement.

EMstop

Stopping criterion for EM-iteration.

maxiter

Maximum number of iterations.

Value

BICmat: Matrix with BIC values
K: Vector with different components
method: Vector with proportional hazard methods
Sdist: Survival distribution

Details

Based on the output BIC matrix, model selection can be performed in terms of the number of mixture components and imposed proportionality restrictions.

Examples

Run this code


##Fitting 3 Weibull proportional hazard models (over groups, pages) for K=2,3 components
data(webshop)
res <- msBIC(webshop, K = c(2,3), method = c("main.p","main.g"), maxiter = 10)
res