emes: Expectation-Maximization (EM) algorithm and...

In geecure: Marginal Proportional Hazards Mixture Cure Models with Generalized Estimating Equations

Description

EM algorithm is based on Peng et al. (2007) and ES algorithm is based on Niu and Peng (2013). ES algorithm is an estension of the EM algorithm where the M-step of the EM algorithm is replaced by a step requiring the solution of a series of generalised estimating equations. Both algorithm are used for the analysis of survival cure data with potential correlation.

Usage

emes(Time, Status, X, Z, id, corstr, stdz, esmax, eps)

Arguments

Time	right censored data which is the follow up time.
Status	the censoring indicator, normally 0 = event of interest happens, and 0 = censoring.
X	a matrix of covariates corresponding to the latency part.
Z	a matrix of covariates corresponding to the incidence part.
id	a vector which identifies the clusters. The length of id should be the same as the number of observations.
corstr	a character string specifying the correlation structure. The following are permitted: independence and exchangeable.
stdz	If it is TRUE, all the covariates in the formula and cureform are standardized. By default, stdz = FALSE.
esmax	specifies the maximum iteration number. If the convergence criterion is not met, the ES iteration will be stopped after esmax iterations and the estimates will be based on the last ES iteration. The default esmax = 100.
eps	tolerance for convergence. The default is eps = 1e-6. Iteration stops once the relative change in deviance is less than eps.

geecure documentation built on May 2, 2019, 6:03 a.m.