bayesGspline {bayesSurv}  R Documentation 
Summary for the density estimate based on the model with Bayesian Gsplines.
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 Gspline.
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 Gspline evaluated in a grid at each iteration of MCMC.
Usage
bayesGspline(dir, 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
Gsplines if more Gsplines were used in one simulation (e.g. with
doublycensored data or in the model where both the error term and the
random intercept were defined as the Gsplines). According to which
 
extens.adjust 
this argument is applicable for the situation when
the MCMC chains were created using the function
In that case the location of the error term and the random intercept
are separately not identifiable. Only the location of the sum
Argument The following values of
 
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 Gspline
was bivariate) at which the sampled densities are to be
evaluated. This item is  
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  
nwrite 
frequency with which is the user informed about the
progress of computation (every  
only.aver 
 
standard 
 
version 
this argument indicates by which

Value
An object of class bayesGspline
is returned. This object is a
list with components
grid
, average
for the univariate Gspline and
components grid1
, grid2
, average
for the bivariate Gspline.
grid 
this is a grid of values (vector) at which the McMC average of the Gspline was computed.  
average 
these are McMC averages of the Gspline (vector) evaluated in
 
grid1 
this is a grid of values (vector) for the first dimension at which the McMC average of the Gspline was computed.  
grid2 
this is a grid of values (vector) for the second dimension at which the McMC average of the Gspline was computed.  
average 
this is a matrix
and

There exists a method to plot objects of the class bayesGspline
.
Attributes
Additionally, the object of class bayesGspline
has the following
attributes:
sample.size
a length of the McMC sample used to compute the McMC average.
sample
Gspline evaluated in a grid of values. This attribute is present only if
only.aver = FALSE
.For a univariate Gspline this is a matrix with
sample.size
columns and length(grid1) rows.For a bivariate Gspline this is a matrix with
sample.size
columns and length(grid1)*length(grid2) rows.
Author(s)
Arnošt Komárek arnost.komarek@mff.cuni.cz
References
Komárek, A. (2006). Accelerated Failure Time Models for Multivariate IntervalCensored Data with Flexible Distributional Assumptions. PhD. Thesis, Katholieke Universiteit Leuven, Faculteit Wetenschappen.
Komárek, A. and Lesaffre, E. (2006). Bayesian semiparametric accelerated failurew time model for paired doubly intervalcensored data. Statistical Modelling, 6, 3–22.
Komárek, A. and Lesaffre, E. (2008). Bayesian accelerated failure time model with multivariate doublyintervalcensored 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
##  extandmobPA.R and
## https://www2.karlin.mff.cuni.cz/~komarek/software/bayesSurv/extandmobPA.pdf
##  extandmobCS.R and
## https://www2.karlin.mff.cuni.cz/~komarek/software/bayesSurv/extandmobCS.pdf
##  exeortc.R and
## https://www2.karlin.mff.cuni.cz/~komarek/software/bayesSurv/exeortc.pdf
##