bayesDensity {bayesSurv}R Documentation

Summary for the density estimate based on the mixture Bayesian AFT model.


Function to summarize the results obtained using bayessurvreg1 function.

Compute the conditional (given the number of mixture components) and unconditional estimate of the density function based on the values sampled using the reversible jumps MCMC (MCMC average evaluated in a grid of values).

Give also the values of each sampled density evaluated at that grid (returned as the attribute of the resulting object). Methods for printing and plotting are also provided.


bayesDensity(dir, stgrid, centgrid, grid, n.grid = 100,
    skip = 0, by = 1, last.iter,
    standard = TRUE, center = TRUE, unstandard = TRUE)



directory where to search for files ‘mixmoment.sim’, ‘mweight.sim’, mmean.sim', ‘mvariance.sim’ with the McMC sample.


grid of values at which the sampled standardized densities are to be evaluated. If missing, the grid is automatically computed.


grid of values at which the sampled centered (but not scaled) densities are to be evaluated. If missing. the grid is automatically computed.


grid of values at which the sampled densities are to be evaluated. If missing, the grid is guessed from the first 20 sampled mixtures as the sequence starting with the minimal sampled mixture mean minus 3 standard deviations of the appropriate mixture component, ending with the maximal sampled mixture mean plus 3 standard deviations of the appropriate mixture component, of the length given by n.grid.


the length of the grid if grid = NULL.


number of rows that should be skipped at the beginning of each *.sim file with the stored sample.


additional thinning of the sample.


index of the last row from *.sim files that should be used. If not specified than it is set to the maximum available determined according to the file mixmoment.sim.


if TRUE then also standardized (zero mean, unit variance) sampled densities are evaluated.


if TRUE then also centered (zero mean) sampled densities are evaluated.


of TRUE then also original (unstandardized) sampled densities are evaluated.


An object of class bayesDensity is returned. This object is a list and has potentially three components: standard, center and unstandard. Each of these three components is a data.frame with as many rows as number of grid points at which the density was evaluated and with columns called ‘grid’, ‘unconditional’ and ‘k = 1’, ..., ‘k = k.max’ giving a predictive errr density, either averaged over all sampled ks (unconditional) or averaged over a psecific number of mixture components.

Additionally, the object of class bayesDensity has three attributes:


a vector of length 1 + kmax giving the frequency of each k in the sample.


a data.frame with columns called ‘intercept’ and ‘scale’ giving the mean and variance of the sampled mixture at each iteration of the McMC.


a data.frame with one column called ‘k’ giving number of mixture components at each iteration.

There exist methods to print and plot objects of the class bayesDensity.


Arnošt Komárek


Komárek, A. (2006). Accelerated Failure Time Models for Multivariate Interval-Censored Data with Flexible Distributional Assumptions. PhD. Thesis, Katholieke Universiteit Leuven, Faculteit Wetenschappen.

Komárek, A. and Lesaffre, E. (2007). Bayesian accelerated failure time model for correlated interval-censored data with a normal mixture as an error distribution. Statistica Sinica, 17, 549–569.


## See the description of R commands for
## the models described in
## Komarek (2006),
## Komarek and Lesaffre (2007),
## R commands available
## in the documentation
## directory of this package
## - ex-cgd.R and
## - ex-tandmobMixture.R and

[Package bayesSurv version 3.7 Index]