bayesCureRateModel (version 1.5)

plot.bayesCureModel: Plot method

Description

Plots and computes HDIs.

Usage

# S3 method for bayesCureModel
plot(x, burn = NULL, alpha0 = 0.05, gamma_mix = TRUE, 
	K_gamma = 5, plot_graphs = TRUE, bw = "nrd0", what = NULL, predict_output = NULL,  
	index_of_main_mode = NULL, draw_legend = TRUE,...)

Value

The function plots graphic output on the plot device if plot_graphs = TRUE. Furthermore, a list of \(100(1-\alpha)\%\) Highest Density Intervals per parameter is returned.

Arguments

x

An object of class bayesCureModel

burn

Number of iterations to discard as burn-in period.

alpha0

A value between 0 and 1 in order to compute the 1-\(\alpha0\) Highest Posterior Density regions.

gamma_mix

Boolean. If TRUE, the density of the marginal posterior distribution of the \(\gamma\) parameter is estimated from the sampled MCMC values by fitting a normal mixture model.

K_gamma

Used only when gamma_mix = TRUE and corresponds to the number of normal mixture components used to estimate the marginal posterior density of the \(\gamma\) parameter.

plot_graphs

Boolean, if FALSE only HDIs are computed.

bw

bandwidth

what

Integer or a character string with possible values equal to 'cured_prob', 'survival' or 'residuals'. An integer entry indicates which parameter should be plotted. If set to NULL (default), all parameters are plotted one by one. If set to 'cured_prob' or 'survival' the estimated cured probability or survival function is plotted, conditional on a set of covariates defined in the p_cured_output argument. In case where what = 'residuals' the residuals of the fitted model are plotted versus the quantity \(-log(S)\) where \(S\) denotes the estimated survival function arising from the Kaplan-Meier estimate based on the residuals and the censoring times.

predict_output

Optional argument which is required only when what = 'cured_prob' or what = 'survival'. It is returned by a call to the predict.bayesCureModel function.

index_of_main_mode

If NULL (default), the whole MCMC output is used for plotting. Otherwise, it is a subset of the retained MCMC iterations in order to identify the main mode of the posterior distribution, as returned by the index_of_main_mode value of the summary.bayesCureRateModel function.

draw_legend

Boolean. If TRUE (default), a legend is plotted in the case where what = 'survival' or what = 'cured_prob'.

...

arguments passed by other methods.

Author

Panagiotis Papastamoulis

Examples

# simulate toy data just for cran-check purposes        
	set.seed(10)
        n = 4
        # censoring indicators
        stat = rbinom(n, size = 1, prob = 0.5)
        # covariates
        x <- matrix(rnorm(2*n), n, 2)
        # observed response variable 
        y <- rexp(n)
#	define a data frame with the response and the covariates        
        my_data_frame <- data.frame(y, stat, x1 = x[,1], x2 = x[,2])
# run a weibull model with default prior setup
# considering 2 heated chains 
	fit1 <- cure_rate_MC3(survival::Surv(y, stat) ~ x1 + x2, data = my_data_frame, 
		promotion_time = list(family = 'exponential'),
		nChains = 2, 
		nCores = 1, 
		mcmc_cycles = 3, sweep=2)
	mySummary <- summary(fit1, burn = 0)
	# plot the marginal posterior distribution of the first parameter in returned mcmc output
	plot(fit1, what = 1, burn = 0)