| plotly_expRMM {mixtools} | R Documentation |
Plot sequences from the EM algorithm for censored mixture of exponentials using plotly
Description
This is an updated function of plotexpRMM. For more technical details, please refer to plotexpRMM.
Usage
plotly_expRMM(a , title = NULL , rowstyle = TRUE , subtitle=NULL,
width = 2 , cex = 2 , col.comp = NULL,
legend.text = NULL, legend.text.size = 15, legend.size = 15,
title.x = 0.5, title.y = 0.95, title.size = 15,
xlab.size = 15, xtick.size = 15,
ylab.size = 15, ytick.size = 15)
Arguments
a |
An object returned by |
title |
The title of the plot, set to some default value if |
rowstyle |
Window organization, for plots in rows (the default) or columns. |
subtitle |
A subtitle for the plot, set to some default value if |
width |
Line width. |
cex |
Size of dots. |
col.comp |
Color of different components. Number of color specified needs to be consistent with number of components. |
legend.text |
Title of legend. |
legend.text.size |
Size of the legend title. |
legend.size |
Size of legend. |
title.size |
Size of the main title. |
title.x |
Horsizontal position of the main title. |
title.y |
Vertical posotion of the main title. |
xlab.size |
Size of the lable of X-axis. |
xtick.size |
Size of tick lables of X-axis. |
ylab.size |
Size of the lable of Y-axis. |
ytick.size |
Size of tick lables of Y-axis. |
Value
The plot returned
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
See Also
Related functions:
expRMM_EM, summary.mixEM, plot.mixEM, plotexpRMM.
Other models and algorithms for censored lifetime data
(name convention is model_algorithm):
weibullRMM_SEM, spRMM_SEM.
Examples
n=300 # sample size
m=2 # number of mixture components
lambda <- c(1/3,1-1/3); rate <- c(1,1/10) # mixture parameters
set.seed(1234)
x <- rexpmix(n, lambda, rate) # iid ~ exponential mixture
cs=runif(n,0,max(x)) # Censoring (uniform) and incomplete data
t <- apply(cbind(x,cs),1,min) # observed or censored data
d <- 1*(x <= cs) # censoring indicator
###### EM for RMM, exponential lifetimes
l0 <- rep(1/m,m); r0 <- c(1, 0.5) # "arbitrary" initial values
a <- expRMM_EM(t, d, lambda=l0, rate=r0, k = m)
summary(a) # EM estimates etc
plotly_expRMM(a , rowstyle = TRUE) # plot of EM sequences