Description Usage Arguments Value Value for bayesBisurvreg.priorInit Value for bayesBisurvreg.priorBeta Author(s)
These functions are not to be called by ordinary users.
These are just subparts of ‘bayesBisurvreg’ function to make it more readable for the programmer.
1 2 3 4 5 6 7 8 9 10  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)

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

store 
a~list as returned by the function

Some lists.
A~list with the following components:
integer arguments for the Gspline constructor in the C++ code related to the onset/event time
double arguments for the Gspline constructor in the C++ code related to the onset/event time
vector of initial values for the log(onset time)/log(event time), sorted as y[1,1],y[1,2], …, y[n,1],y[n,2] in the case of bivariate response with sample size equal to n
initial component labels (vector of size n) taking values from 1 to the total length of the Gspline related to the onset/event time
integer arguments for the Gspline constructor in the C++ code related to timetoevent in the case of doubly censoring
double arguments for the Gspline constructor in the C++ code related to timetoevent in the case of doubly censoring
vector of initial values for the timetoevent in the case of doubly censoring sorted as
y[1,1],y[1,2], …, y[n,1],y[n,2]
in the case of bivariate response with sample size equal to n
initial component labels (vector of size n) taking values from 1 to the total length of the Gspline related to timetoevent in the case of doubly censoring
index of the nullth iteration
2 component vector (one component for onset, one
for timetoevent), specification of the Gspline model (1 or 2), see
bayesHistogram
for more detail
lower limit of the logresponse (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
upper limit of the logresponse 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 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
lower limit of the response as required by the C++
function bayesBisurvreg
, related to timetoevent in the
case of doubly censoring, equal to 0 if there is no doublycensoring
upper limit of the response as required by the C++
function bayesBisurvreg
, related to timetoevent in the
case of doubly censoring, equal to 0 if there is no doublycensoring
status vector related to timetoevent in the case of doubly censoring, equal to 0 otherwise.
and the following attributes:
init 
prior 
mcmc.par 
init2 
prior2 
mcmc.par2 
A~list with the following components:
integer arguments for C++ classBetaGamma
constructor
double arguments for C++ classBetaGamma
constructor
and the following attributes:
a~vector with initial values of the beta parameter,
equal to numeric(0)
if there are no regressors
a~list with components mean.prior
and
var.prior
containing vectors with the prior mean and prior
variance of the beta
parameters
Arno<c5><a1>t Kom<c3><a1>rek arnost.komarek[AT]mff.cuni.cz
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