bayesBisurvreg.help {bayesSurv} | R Documentation |
Helping function for Bayesian regression with smoothed bivariate densities as the error term, based on possibly censored data
Description
These functions are not to be called by ordinary users.
These are just sub-parts of ‘bayesBisurvreg’ function to make it more readable for the programmer.
Usage
bayesBisurvreg.checkStore(store)
bayesBisurvreg.priorInit(dim, prior, init, design, mcmc.par,
prior2, init2, design2, mcmc.par2,
doubly)
bayesBisurvreg.priorBeta(prior.beta, init, design)
bayesBisurvreg.writeHeaders(dir, dim, nP, doubly, prior.init, store,
design, design2)
Arguments
store |
a list as required by the argument |
dim |
dimension of the response, 1 or 2 |
prior |
a list as required by the argument |
prior2 |
a list as required by the argument |
init |
a list as required by the argument |
init2 |
a list as required by the argument |
mcmc.par |
a list as required by the argument |
mcmc.par2 |
a list as required by the argument |
design |
an object as returned by the function
|
design2 |
an object as returned by the function
|
doubly |
logical indicating whether the response is doubly censored or not |
prior.beta |
a list as required by the argument |
dir |
path to the directory where the sampled values are to be stored |
nP |
sample size - number of observations if the univariate model is fitted, number of bivariate observational vectors if the bivariate model is fitted |
prior.init |
a list as returned by the function
|
Value
Some lists.
Value for bayesBisurvreg.priorInit
A~list with the following components:
- Gparmi
integer arguments for the G-spline constructor in the C++ code related to the onset/event time
- Gparmd
double arguments for the G-spline constructor in the C++ code related to the onset/event time
- y
vector of initial values for the log(onset time)/log(event time), sorted as
y_1[1], y_1[2], \dots, y_n[1], y_n[2]
in the case of bivariate response with sample size equal ton
- r
initial component labels (vector of size
n
) taking values from 1 to the total length of the G-spline related to the onset/event time- Gparmi2
integer arguments for the G-spline constructor in the C++ code related to time-to-event in the case of doubly censoring
- Gparmd2
double arguments for the G-spline constructor in the C++ code related to time-to-event in the case of doubly censoring
- y2
vector of initial values for the time-to-event in the case of doubly censoring sorted as
y_1[1], y_1[2], \dots, y_n[1], y_n[2]
in the case of bivariate response with sample size equal to
n
- r2
initial component labels (vector of size
n
) taking values from 1 to the total length of the G-spline related to time-to-event in the case of doubly censoring- iter
index of the nullth iteration
- specification
2 component vector (one component for onset, one for time-to-event), specification of the G-spline model (1 or 2), see
bayesHistogram
for more detail- y.left
lower limit of the log-response (or exact/right/left censored observation) as required by the C++ function
bayesBisurvreg
, related to the onset time in the case of doubly censoring and to the event time otherwise- y.right
upper limit of the log-response as required by the C++ function
bayesBisurvreg
, related to the onset time in the case of doubly censoring and to the event time otherwise- status
status vector as required by the C++ function
bayesBisurvreg
related to the onset time in the case of doubly censoring and to the event time otherwise- t2.left
lower limit of the response as required by the C++ function
bayesBisurvreg
, related to time-to-event in the case of doubly censoring, equal to 0 if there is no doubly-censoring- t2.right
upper limit of the response as required by the C++ function
bayesBisurvreg
, related to time-to-event in the case of doubly censoring, equal to 0 if there is no doubly-censoring- status2
status vector related to time-to-event in the case of doubly censoring, equal to 0 otherwise.
and the following attributes:
init |
prior |
mcmc.par |
init2 |
prior2 |
mcmc.par2 |
Value for bayesBisurvreg.priorBeta
A list with the following components:
- parmI
integer arguments for C++
classBetaGamma
constructor- parmD
double arguments for C++
classBetaGamma
constructor
and the following attributes:
- init
a~vector with initial values of the beta parameter, equal to
numeric(0)
if there are no regressors- prior.beta
a~list with components
mean.prior
andvar.prior
containing vectors with the prior mean and prior variance of thebeta
parameters
Author(s)
Arnošt Komárek arnost.komarek@mff.cuni.cz