plotweibullRMM: Plot sequences from the Stochastic EM algorithm for mixture of Weibull

Description

Function for plotting sequences of estimates along iterations, from an object returned by weibullRMM_SEM, a Stochastic EM algorithm for mixture of Weibull distributions with randomly right censored data (see reference below).

Usage

plotweibullRMM(a, title = NULL, rowstyle = TRUE, subtitle = NULL, ...)

Value

The plot returned

Arguments

a: An object returned by weibullRMM_SEM.
title: The title of the plot, set to some default value if NULL.
rowstyle: Window organization, for plots in rows (the default) or columns.
subtitle: A subtitle for the plot, set to some default value if NULL.
...: Other parameters (such as lwd) passed to plot, lines, and legend commands.

Author

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

Run this code

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) # default plot of St-EM sequences

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