predictive {bayesSurv}R Documentation

Compute predictive quantities based on a Bayesian survival regression model.

Description

This function runs additional McMC to compute predictive survivor and hazard curves and predictive event times for specified values of covariates.

Firstly, the function bayessurvreg1 has to be used to obtain a sample from the posterior distribution of unknown quantities.

Directly, posterior predictive quantiles and means of asked quantities are computed and stored in files.

Function predictive.control serves only to perform some input checks inside the main function predictive.

Usage

predictive(
     formula,
     random,
     time0 = 0,
     data = parent.frame(),
     grid,
     type,
     subset,
     na.action = na.fail,
     quantile = c(0, 0.025, 0.5, 0.975, 1),                       
     nsimul = list(niter = 10, nwrite = 10),
     predict = list(Et=TRUE, t=FALSE, Surv=TRUE,
                    hazard=FALSE, cum.hazard=FALSE),
     store = list(Et=TRUE, t = FALSE, Surv = FALSE,
                  hazard = FALSE, cum.hazard=FALSE),
     Eb0.depend.mix = FALSE,
     dir = getwd(),
     toler.chol = 1e-10,
     toler.qr = 1e-10,
     ...)

predictive.control(predict, store, quantile)

Arguments

formula same formula as that one used to sample from the posterior distribution of unknown quantities by the function bayessurvreg1.
random same random statement as that one used to sample from the posterior distribution of unknown quantities by the function bayessurvreg1.
time0 starting time for the survival model. This option is used to get correct hazard function in the case that the original model was log(T - time0) = ....
data optional data frame in which to interpret the variables occuring in the formulas. Usually, you create a new data.frame similar to that one used to obtain a sample from the posterior distribution. In this new data.frame, put covariate values equal to these for which predictive quantities are to be obtained. If cluster statement was used, assign a unique cluster identification to each observation. Response variable and a censoring indicator may be set to arbitrary values. They are only used in formula but are ignored for computation.
grid a list of length as number of observations in data or a vector giving grids of values where predictive survivor functions, hazards, cumulative hazards are to be evaluated. If it is a vector, same grid is used for all observations from data. Not needed if only predict$t or predict$Et are TRUE. If time0 is different from zero your grid should start at time0 and not at zero.
type a character string giving the type of assumed error distribution. Currently, valid are substrings of "mixture". In the future, "spline", "polya.tree" might be also implemented.
subset subset of the observations from the data to be used. This option will normally not be needed.
na.action function to be used to handle any NAs in the data. The user is discouraged to change a default value na.fail.
quantile a vector of quantiles that are to be computed for each predictive quantity.
nsimul a list giving the length of the simulation used to sample from posterior predictive distribution. It should be consistent with already simulated values obtained by bayessurvreg1 function. The list has the following components.
niter
number of iterations that are to be performed. It should not be higher than the number already simulated values.
nwrite
interval in which predictive quantities are written to files.
predict a list of logical values indicating which predictive quantities are to be sampled. Components of the list:
Et
predictive expectations of survivor times
t
predictive survivor times
Surv
predictive survivor functions
hazard
predictive hazard functions
cum.hazard
predictive cumulative hazard functions
store a list of logical values indicating which predictive quantities are to be stored in files as `predET*.sim', `predT*.sim', `predS*.sim', `predhazard*.sim', `predcumhazard*.sim'. If you are interested only in posterior means or quantiles of the predictive quantities you do not have to store sampled values. Posterior means and quantiles are stored in files `quantET*.sim', `quantT*.sim', `quantS*.sim', `quanthazard*.sim', `quantpredhazard*.sim'.
Eb0.depend.mix a logical value indicating whether the mean of the random intercept (if included in the model) was given in a hierarchical model as an overall mean of the mixture in the error term. With FALSE (default) you have the same model as that one described in an accompanying paper. An ordinary user is discouraged from setting this to TRUE.
dir a string giving a directory where previously simulated values were stored and where newly obtained quantities will be stored. On Unix, do not use `~/' to specify your home directory. A full path must be given, e.g. `/home/arnost/'.
toler.chol tolerance for the Cholesky decomposition.
toler.qr tolerance for the QR decomposition.
... who knows?

Value

An integer which should be equal to zero if everything ran fine.

Author(s)

Arnost Komarek arnost.komarek@med.kuleuven.ac.be

Examples

  ## See attached files.

[Package bayesSurv version 0.1 Index]