predictive2: Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions.

Description

This function computes predictive densities, survivor and hazard curves for specified combinations of covariates.

Firstly, either the function bayesBisurvreg or the function bayessurvreg2 or the function bayessurvreg3 has to be used to obtain a sample from the posterior distribution of unknown quantities.

Function predictive2.control serves only to perform some input checks inside the main function predictive2.

Usage

predictive2(formula, random, obs.dim, time0, data = parent.frame(),
     grid, na.action = na.fail, Gspline,
     quantile = c(0, 0.025, 0.5, 0.975, 1),
     skip = 0, by = 1, last.iter, nwrite,
     only.aver = TRUE,
     predict = list(density=FALSE, Surv=TRUE,
                    hazard=FALSE, cum.hazard=FALSE),
     dir = getwd(), extens = "", extens.random="_b", version=0)
predictive2Para(formula, random, obs.dim, time0, data = parent.frame(),
     grid, na.action = na.fail, Gspline,
     quantile = c(0, 0.025, 0.5, 0.975, 1),
     skip = 0, by = 1, last.iter, nwrite,
     only.aver = TRUE,
     predict = list(density=FALSE, Surv=TRUE,
                    hazard=FALSE, cum.hazard=FALSE),
     dir = getwd(), extens = "", extens.random="_b", version=0)
predictive2.control(predict, only.aver, quantile, obs.dim,
     time0, Gspline, n)

Arguments

formula

the same formula as that one used to sample from the posterior distribution of unknown quantities by the function bayesBisurvreg or bayessurvreg2

random

the same random statement as that one used to sample from the
  posterior distribution of unknown quantities by the function
  bayessurvreg2 or

obs.dim

a vector that has to be supplied if bivariate data were
  used for estimation (using the function
  bayesBisurvreg). This vector has to be of the same
  length as the number of covariate combinations

time0

a~vector of length Gspline$dim giving the starting
  time for the survival model. It does not have to be supplied if equal
  to zero (usually).
  This option is used to get hazard and density functions on the
  original time scale in the case

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

grid

a~vector giving the grid of values where predictive
  quantities are to be evaluated. The grid should normally start at some
  value slightly higher than time0.

na.action

function to be used to handle any NAs in the
  data. The user is discouraged to change a default value
  na.fail.

Gspline

a~list specifying the G-spline used for the error
  distribution in the model. It is a~list with the following components:
  [object Object],[object Object]

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 nwriteth iteration count of
  iterations change).

only.aver

if TRUE only posterior predictive mean is
  computed for all quantities and no quantiles.
  The word of warning: with only.aver set to FALSE, all
  quantities must be stored for all iterations of the MCMC to be able

predict

a list of logical values indicating which predictive quantities are to be computed.
  Components of the list:
  [object Object],[object Object],[object Object],[object Object]

dir

directory where to search for files (`mixmoment.sim',
  `mweight.sim', mmean.sim', gspline.sim', 'beta.sim', 'D.sim', ...) with the McMC
  sample.

extens

an extension used to distinguish different sampled
  G-splines if more formulas were used in one MCMC simulation (e.g. with
  doubly-censored data).
  
if the data were not doubly-censored or if you wish to compute
    predictive quantities for th

extens.random

only applicable if the function
  bayessurvreg3 was used to generate the MCMC sample.
  This is an extension used to distinguish different sampled G-splines
  determining the distribution of the rand

version

this argument indicates by which bayes*survreg* function the
  chains used by bayesGspline were created. Use the following:
  [object Object],[object Object],[object Object],[object Object]

n

number of covariate combinations for which the prediction will
  be performed.

`Value`

A list with possibly the following components (what is included depends
on the value of the arguments predict and only.aver):
grida~vector with the grid values at which the survivor
    function, survivor density, hazard and cumulative hazard are computed.
Survpredictive survivor functions.
    A~matrix with as many columns as length(grid) and as many
    rows as the number of covariate combinations for which the
    predictive quantities were asked. One row per covariate combination.
densitypredictive survivor densities.
    The same structure as Surv component of the list.
hazardpredictive hazard functions.
    The same structure as Surv component of the list.
cum.hazardpredictive cumulative hazard functions.
    The same structure as Surv component of the list.
quant.Survpointwise quantiles for the predictive survivor
    functions.
    This is a list with as many components as the number of covariate
    combinations. One component per covariate combination.
    Each component of this list is a~matrix with as many columns as
    length(grid) and as many rows as the length of the argument
    quantile. Each row of this matrix gives values of one
    quantile. The rows are also labeled by the probabilities (in %) of
    the quantiles.
quant.densitypointwise quantiles for the predictive survivor
    densities.
    The same structure as quant.Surv component of the list.
quant.hazardpointwise quantiles for the predictive hazard
    functions.
    The same structure as quant.Surv component of the list.
quant.cum.hazardpointwise quantiles for the predictive
    cumulative hazard functions.
    The same structure as quant.Surv component of the list.

`References`

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. (2008).
  Bayesian accelerated failure time model with multivariate doubly-interval-censored data
  and flexible distributional assumptions.
  Journal of the American Statistical Association, 103, 523--533.
  Komárek, A. and Lesaffre, E. (2006).
  Bayesian semi-parametric accelerated failurew time model for paired
  doubly interval-censored data.
  Statistical Modelling, 6, 3--22.
  
  Komárek, A.,  Lesaffre, E., and Legrand, C. (2007).
  Baseline and treatment effect heterogeneity for survival
  times between centers using a random effects accelerated failure time
  model with flexible error distribution.
  Statistics in Medicine, 26, 5457--5472.

`Examples`

Run this code## See the description of R commands for
## the models described in
## Komarek (2006),
## Komarek and Lesaffre (2006),
## Komarek and Lesaffre (2008),
## Komarek, Lesaffre, and Legrand (2007).
##
## R commands available in the documentation
## directory of this package
##  - ex-tandmobPA.R and
##    http://www.karlin.mff.cuni.cz/~komarek/software/bayesSurv/ex-tandmobPA.pdf
##  - ex-tandmobCS.R and
##    http://www.karlin.mff.cuni.cz/~komarek/software/bayesSurv/ex-tandmobCS.pdf
##  - ex-eortc.R and
##    http://www.karlin.mff.cuni.cz/~komarek/software/bayesSurv/ex-eortc.pdf
Run the code above in your browser using DataLab