bayesSurv (version 2.4)

bayesGspline: Summary for the density estimate based on the model with Bayesian G-splines.

Description

Compute the estimate of the density function based on the values sampled using the MCMC (MCMC average evaluated in a grid of values) in a model where density is specified as a Bayesian G-spline.

This function serves to summarize the MCMC chains related to the distributional parts of the considered models obtained using the functions: bayesHistogram, bayesBisurvreg, bayessurvreg2, bayessurvreg3.

If asked, this function returns also the values of the G-spline evaluated in a grid at each iteration of MCMC.

Usage

bayesGspline(dir = getwd(), extens="", extens.adjust="_b",
   grid1, grid2, skip = 0, by = 1, last.iter, nwrite,
   only.aver = TRUE, standard = FALSE, version = 0)

Arguments

dir

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

extens

an extension used to distinguish different sampled G-splines if more G-splines were used in one simulation (e.g. with doubly-censored data or in the model where both the error term and the random intercept were defined as the G-splines). According t

extens.adjust

this argument is applicable for the situation when the MCMC chains were created using the function bayessurvreg3, and when both the distribution of the error term and the random intercept was sp

grid1

grid of values from the first dimension at which the sampled densities are to be evaluated.

grid2

grid of values from the second dimension (if the G-spline was bivariate) at which the sampled densities are to be evaluated. This item is missing if the G-spline is univariate.

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

TRUE/FALSE, if TRUE only MCMC average is returned otherwise also values of the G-spline at each iteration are returned (which might ask for quite lots of memory).

standard

TRUE/FALSE, if TRUE, each G-spline is standardized to have zero mean and unit variance. Only applicable if version = 30 or 31, otherwise standard is always set to FALSE.

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]

Value

• An object of class bayesGspline is returned. This object is a list with components grid, average for the univariate G-spline and components grid1, grid2, average for the bivariate G-spline.
• grid
  this is a grid of values (vector) at which the McMC average of the G-spline was computed.
• average
  these are McMC averages of the G-spline (vector) evaluated in grid.
• grid1
  this is a grid of values (vector) for the first dimension at which the McMC average of the G-spline was computed.
• grid2
  this is a grid of values (vector) for the second dimension at which the McMC average of the G-spline was computed.
• average
  this is a matrix length(grid1) times length(grid2) with McMC averages of the G-spline evaluated in lccc{ x1 = ( grid1 ... grid1 ) } and lrcl{ ( grid2 ) x2 = ( ... ) ( grid2 ) }
• There exists a method to plot objects of the class bayesGspline.

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. (2006). Bayesian semi-parametric accelerated failurew time model for paired doubly interval-censored data. Statistical Modelling, 6, 3--22. 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., 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

## 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
##