plot.bayesCureModel: Plot method

In bayesCureRateModel: Bayesian Cure Rate Modeling for Time-to-Event Data

Plot method

Description

Plots and computes HDIs.

Usage

## S3 method for class 'bayesCureModel'
plot(x, burn = NULL, alpha = 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,...)

Arguments

x	An object of class bayesCureModel
burn	Number of iterations to discard as burn-in period.
alpha	A value between 0 and 1 in order to compute the 1-\alpha 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.

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.

Author(s)

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(distribution = '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)

bayesCureRateModel documentation built on Oct. 4, 2024, 1:07 a.m.