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

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.

See also

Related functions: spRMM_SEM , plotspRMM.

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

Author

Didier Chauveau

Examples

if (FALSE) { # \dontrun{
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"
} # }