predictive {bayesSurv} | R Documentation

## Compute predictive quantities based on a Bayesian survival regression model fitted using bayessurvreg1 function.

### 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 = "mixture", subset, na.action = na.fail,
         quantile = c(0, 0.025, 0.5, 0.975, 1),
         skip = 0, by = 1, last.iter, nwrite, only.aver = FALSE,
         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, only.aver, quantile)

### Arguments

`formula` | the same formula as that one used to sample from the posterior distribution of unknown quantities by the function `bayessurvreg1`.
`random` | the 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 `NA`s 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.
`skip` | number of rows that should be skipped at the beginning of each *.sim file with the stored sample.
`by` | additional thinning of the sample.
`last.iter` | 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`.
`nwrite` | frequency with which is the user informed about the progress of computation (every `nwrite`th iteration count of iterations change).
`only.aver` | if `TRUE` only posterior predictive mean is computed for all quantities and no quantiles.
`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.

### Value

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

### Author(s)

Arnost Komarek arnost.komarek@med.kuleuven.be

### References

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

Komarek, A., Lesaffre, E. (2005). Bayesian accelerated failure time model for correlated interval-censored data with a normal mixture as an error distribution. Submitted.
See `Komarek_Lesaffre_2005.pdf`.

### Examples

    ## See the description of R commands for
    ## the models described in
    ## Komarek (2006),
    ## Komarek and Lesaffre (2005).
    ##
    ## R commands available
    ## in the documentation
    ## directory of this package as
    ## cgd.pdf, cgd.R
    ## tandmobMixture.pdf, tandmobMixture.R

[Package bayesSurv version 0.4 Index]