Plot sequences from the Stochastic EM algorithm for mixture of Weibull using plotly

This is an updated version of plotweibullRMM function by using plotly function. For technical details, please refer to plotweibullRMM.

Usage

plotly_weibullRMM(a, title=NULL, rowstyle=TRUE, subtitle=NULL,
                    width = 3 , col = NULL , 
                    title.size = 15 , title.x = 0.5 , title.y = 0.95,
                    xlab = "Iterations" , xlab.size = 15 , xtick.size = 15,
                    ylab = "Estimates" , ylab.size = 15 , ytick.size = 15,
                    legend.size = 15)

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.

width

Line width.

col

Color of lines. Number of colors specified needs to be consistent with number of components.

title.size

Size of the main title.

title.x

Horsizontal position of the main title.

title.y

Vertical posotion of the main title.

xlab

Label of X-axis.

xlab.size

Size of the lable of X-axis.

xtick.size

Size of tick lables of X-axis.

ylab

Label of Y-axis.

ylab.size

Size of the lable of Y-axis.

ytick.size

Size of tick lables of Y-axis.

legend.size

Size of legend.

Value

The plot returned.

See also

Related functions: weibullRMM_SEM, summary.mixEM, plotweibullRMM.

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

Author

Didier Chauveau

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
plotly_weibullRMM(a , legend.size = 20) # plot of St-EM sequences