msBIC: PHM model selection with BIC

In mixPHM: Mixtures of Proportional Hazard Models

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

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

Details

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

Value

Returns an object of class BICmat with the following values:

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

See Also

screeBIC

Examples

##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 

mixPHM documentation built on May 2, 2019, 8:16 a.m.