weibullRMM_SEM: St-EM algorithm for Reliability Mixture Models (RMM) of...
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weibullRMM_SEM

R Documentation

St-EM algorithm for Reliability Mixture Models (RMM) of Weibull with right Censoring

Description

Parametric Stochastic EM (St-EM) algorithm for univariate finite mixture of Weibull distributions with randomly right censored data.

Usage

weibullRMM_SEM(x, d = NULL, lambda = NULL, shape = NULL, scale = NULL,
        k = 2, maxit = 200, maxit.survreg = 200, epsilon = 1e-03,
        averaged = TRUE, verb = FALSE)

Arguments

`x`	A vector of n real positive lifetime (possibly censored) durations. If `d` is not `NULL` then a vector of random censoring times `c` occured, so that x= min(x,c) and d = I(x <= c).
`d`	The vector of censoring indication, where 1 means observed lifetime data, and 0 means censored lifetime data.
`lambda`	Initial value of mixing proportions. If `NULL`, then `lambda` is set to `rep(1/k,k)`.
`shape`	Initial value of Weibull component shapes, all set to 1 if `NULL`.
`scale`	Initial value of Weibull component scales, all set to 1 if `NULL`.
`k`	Number of components of the mixture.
`maxit`	The number of iterations allowed, since for St-EM algorithms convergence is not based on stabilization, exactly `maxit` iterations are performed (see Bordes L. and Chauveau D. (2016) reference below).
`maxit.survreg`	The number of iterations allowed in the computations of the MLE for censored weibull data from the `survival` package (see Bordes L. and Chauveau D. (2016) reference below).
`epsilon`	Tolerance parameter used in the numerical computations of the MLE for censored weibull data by `survreg` from the `survival` package (see Bordes L. and Chauveau D. (2016) reference below).
`averaged`	The way of updating parameters at each iteration: if `TRUE`, current values of the parameters are obtained by averaging the sequence (see Bordes L. and Chauveau D. (2016) reference below).
`verb`	If TRUE, print updates for every iteration of the algorithm as it runs

Details

This St-EM algorithm calls functions from the survival package to compute parametric MLE for censored weibull data.

Value

weibullRMM_SEM returns a list of class "mixEM" with the following items:

`x`	The input data.
`d`	The input censoring indicator.
`lambda`	The estimates for the mixing proportions.
`scale`	The estimates for the Weibull component scales.
`shape`	The estimates for the Weibull component shapes.
`loglik`	The log-likelihood value at convergence of the algorithm.
`posterior`	An n x k matrix of posterior probabilities for observation, after convergence of the algorithm.
`all.loglik`	The sequence of log-likelihoods over iterations.
`all.lambda`	The sequence of mixing proportions over iterations.
`all.scale`	The sequence of component scales over iterations.
`all.shape`	The sequence of component shapes over iterations.
`ft`	A character vector giving the name of the function called.

Author(s)

Didier Chauveau

References

Bordes, L., and Chauveau, D. (2016), Stochastic EM algorithms for parametric and semiparametric mixture models for right-censored lifetime data, Computational Statistics, Volume 31, Issue 4, pages 1513-1538. https://link.springer.com/article/10.1007/s00180-016-0661-7

Examples

n = 500 # sample size
m = 2 # nb components
lambda=c(0.4, 0.6)
shape <- c(0.5,5); scale <- c(1,20) # model parameters
set.seed(321)
x <- rweibullmix(n, lambda, shape, scale) # iid ~ weibull mixture
cs=runif(n,0,max(x)+10) # iid censoring times
t <- apply(cbind(x,cs),1,min) # censored observations
d <- 1*(x <= cs)              # censoring indicator

## set arbitrary or "reasonable" (e.g., data-driven) initial values
l0 <- rep(1/m,m); sh0 <- c(1, 2); sc0 <- c(2,10)
# Stochastic EM algorithm 
a <- weibullRMM_SEM(t, d, lambda = l0, shape = sh0, scale = sc0, maxit = 200)

summary(a) # Parameters estimates etc
plotweibullRMM(a) # plot of St-EM sequences
plot(a, which=2) # or equivalently, S3 method for "mixEM" object

mixtools documentation built on Dec. 5, 2022, 5:23 p.m.