stableEM {mixPHM} | R Documentation |
This function performs the clustering for different EM starting values in order to find a stable solution.
stableEM(x, K, numEMstart = 5, method = "separate", Sdist = "weibull", cutpoint = NULL, EMoption = "classification", EMstop = 0.0001, maxiter = 1000, print.likvec = TRUE)
x |
Data frame or matrix of dimension n*p with survival times (NA 's allowed). |
K |
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. |
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.
Returns an object of class mws
with the following values:
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 |
## 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)