bayesGspline {bayesSurv} | R Documentation |
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.
bayesGspline(dir = getwd(), extens="", extens.adjust="_b", grid1, grid2, skip = 0, by = 1, last.iter, nwrite, only.aver = TRUE, standard = FALSE, version = 0)
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 to which
bayes*survreg* function was used, specify the argument
extens in the following way.
| ||
extens.adjust |
this argument is applicable for the situation when
the MCMC chains were created using the function
bayessurvreg3 , i.e. when both the distribution of the
error term and the random intercept was specified as the G-spline.
In that case the location of the error term and the random intercept are separately not identifiable. Only the location of the sum epsilon + b can be estimated. For this reason, the function bayesGspline always centers the distribuytion of
the random intercept to have a zero mean and adds its original mean to
the mean of the distribution of the error term.
Argument extens.adjust is used to match correctly the files
containing the G-spline of the random intercept corresponding to the
particular error term.
The following values of extens.adjust should be used in the
following situations:
| ||
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 nwrite th 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:
|
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
|
There exists a method to plot objects of the class bayesGspline
.
Additionally, the object of class bayesGspline
has the following
attributes:
sample.size
sample
only.aver = FALSE
.
For a univariate G-spline this is a matrix with sample.size
columns and
length(grid1) rows.
For a bivariate G-spline this is a matrix
with sample.size
columns and
length(grid1)*length(grid2) rows.
Arnost Komarek arnost.komarek@med.kuleuven.be
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. and Lesaffre, E. (2006).
Bayesian accelerated failure time model with multivariate doubly-interval-censored data
and flexible distributional assumptions.
Submitted.
See Komarek_Lesaffre_2006.pdf
.
Komarek, A. and Lesaffre, E. (2006b).
Bayesian semiparametric accelerated failurew time model for paired
doubly-interval-censored data.
Submitted.
See Komarek_Lesaffre_2006b.pdf
.
Komarek, A. Lesaffre, E., and Legrand, C. (2006). EORTC data article. To be written.
## See the description of R commands for ## the models described in ## Komarek (2006), ## Komarek and Lesaffre (2006), ## Komarek and Lesaffre (2006b), ## Komarek, Lesaffre, and Legrand (2006). ## ## R commands available (or soon available) ## in the documentation ## directory of this package ## as tandmobCS.pdf, tandmobCS.R ## tandmobPA.pdf, tandmobPA.R. ##