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 , and 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 distribution 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.
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz
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. (2007). Bayesian accelerated failure time model with multivariate doubly-interval-censored data and flexible distributional assumptions. To appear in Journal of the American Statistical Association.
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. To appear in Statistics in Medicine.
## See the description of R commands for ## the models described in ## Komarek (2006), ## Komarek and Lesaffre (2006), ## Komarek and Lesaffre (2007), ## Komarek, Lesaffre, and Legrand (2007). ## ## R commands available ## in the documentation ## directory of this package ## as tandmobPA.pdf, tandmobPA.R, ## tandmobCS.pdf, tandmobCS.R, ## eortc.pdf. ##