summary.predict_bayesCureModel: Summary method for predictions.

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

Summary method for predictions.

Description

This function produces MCMC summaries for an object of class predict_bayesCureModel.

Usage

## S3 method for class 'predict_bayesCureModel'
summary(object, ...)

Arguments

object	An object of class predict_bayesCureModel.
...	Other options passed to the summary.mcmc method of the coda package.

Value

A list with the following entries

survival	MCMC summaries (quantiles) for the survival function.
cured_probability	MCMC summaries (quantiles) for the conditional cured probability.
cumulative_hazard	MCMC summaries (quantiles) for the cumulative hazard function.
hazard_rate	MCMC summaries (quantiles) for the hazard rate function.

Author(s)

Panagiotis Papastamoulis

References

Papastamoulis and Milienos (2024). Bayesian inference and cure rate modeling for event history data. TEST doi: 10.1007/s11749-024-00942-w.

See Also

cure_rate_MC3

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)
	newdata <- data.frame(x1 = c(0.2,-1), x2 = c(-1,0))
	# return predicted values at tau = c(0.5, 1)
	my_prediction <- predict(fit1, newdata = newdata, 
	     burn = 0, tau_values = c(0.5, 1))
	my_summary <- summary(my_prediction, quantiles = c(0.1,0.9))		

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