Plot sequences from the Stochastic EM algorithm for mixture of Weibull
plotweibullRMM.RdFunction 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).
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 toplot,lines, andlegendcommands.
See also
Related functions:
weibullRMM_SEM, summary.mixEM.
Other models and algorithms for censored lifetime data
(name convention is model_algorithm):
expRMM_EM,
spRMM_SEM
.
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)
#> number of iterations = 200
summary(a) # Parameters estimates etc
#> summary of weibullRMM_SEM object:
#> comp 1 comp 2
#> lambda 0.396100 0.60390
#> shape 0.497675 5.18371
#> scale 0.620329 20.20621
#> loglik at estimate: -960.1892
#> 25 % of the data right censored
plotweibullRMM(a) # default plot of St-EM sequences
