stableEM: Stable EM solution

Description

This function performs the clustering for different EM starting values in order to find a stable solution.

Usage

stableEM(x, K, numEMstart = 5, method = "separate", Sdist = "weibull", cutpoint = NULL,
EMoption = "classification", EMstop = 0.0001, maxiter = 1000, print.likvec = TRUE)

Arguments

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

Number of mixture components.

numEMstart

Number of different starting solutions

method

Imposing proportionality restrictions on the hazards: 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.

Sdist

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

cutpoint

Integer value with upper bound for observed dwell times. Above this cutpoint, values are regarded as censored. If NULL, no censoring is performed

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.

print.likvec

If TRUE the likelihood values for different starting solutions are printed.

Value

K: Number of components
iter: Number of EM iterations
method: Method with propotionality restrictions used for estimation
Sdist: Assumed survival distribution
likelihood: Log-likelihood value for each iteration
pvisit: Matrix of prior probabilities due to NA structure
se.pvisit: Standard errors for priors
shape: Matrix with shape parameters
scale: Matrix with scale parameters
group: Final deterministic cluster assignment
posteriors: Final probabilistic cluster assignment
npar: Number of estimated parameters
aic: Akaike information criterion
bic: Bayes information criterion
clmean: Matrix with cluster means
se.clmean: Standard errors for cluster means
clmed: Matrix with cluster medians

Details

After the computation of the models for different starting solutions using the function phmclust the best model is chosen, i.e., the model with the largest likelihood value. The output values refer to this final model.

Examples

Run this code


## Exponental mixture model with 2 components for 4 different starting solutions
data(webshop)
res <- stableEM(webshop, K = 2, numEMstart = 4, Sdist = "exponential")
res
summary(res)