# 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`. ```r 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. ## Returns 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](https://link.springer.com/article/10.1007/s00180-016-0661-7) ## Author(s) Didier Chauveau ## Examples ```r 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) summary(a) # Parameters estimates etc plotly_weibullRMM(a , legend.size = 20) # plot of St-EM sequences ```

plotly_weibullRMM function