plotly_spRMM: Plot output from Stochastic EM algorithm for semiparametric...

In mixtools: Tools for Analyzing Finite Mixture Models

Plot output from Stochastic EM algorithm for semiparametric scaled mixture of censored data using plotly.

Description

This is an updated version of plotspRMM function. For technical details, please refer to plotspRMM.

Usage

  plotly_spRMM(sem, tmax = NULL,
               width = 3 , col = '#1f77b4', cex = 3,
               title.size = 15 , 
               title.x = 0.5 , title.y = 0.95,
               xlab.size = 15 , xtick.size=15 ,
               ylab.size = 15 , ytick.size=15)

Arguments

sem	An object returned by spRMM_SEM.
tmax	The max time for x axis, set to some default value if NULL.
width	Width of lines.
col	Color of lines.
cex	Size of dots.
title.size	Size of the main title.
title.x	Horizontal position of the main title.
title.y	Vertical position of the main title.
xlab.size	Size of the label of X-axis.
xtick.size	Size of the tick of X-axis.
ylab.size	Size of the label of Y-axis.
ytick.size	Size of the tick of Y-axis.

Value

The four plots 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: spRMM_SEM , plotspRMM.

Other models and algorithms for censored lifetime data (name convention is model_algorithm): expRMM_EM, weibullRMM_SEM.

Examples

## Not run: 
n=500 # sample size
m=2 # nb components
lambda=c(0.4, 0.6) # parameters
meanlog=3; sdlog=0.5; scale=0.1
set.seed(12)
# simulate a scaled mixture of lognormals
x <- rlnormscalemix(n, lambda, meanlog, sdlog, scale)
cs=runif(n,20,max(x)+400) # Censoring (uniform) and incomplete data
t <- apply(cbind(x,cs),1,min)
d <- 1*(x <= cs)
tauxc <- 100*round( 1-mean(d),3)
cat(tauxc, "percents of data censored.\n")

c0 <- c(25, 180) # data-driven initial centers (visible modes)
sc0 <- 25/180    # and scaling
s <- spRMM_SEM(t, d, scaling = sc0, centers = c0, bw = 15, maxit = 100)

plotly_spRMM(s) # default
summary(s)   # S3 method for class "spRMM"

## End(Not run)

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