bayesBisurvreg.help: Helping function for Bayesian regression with smoothed...

In bayesSurv: Bayesian Survival Regression with Flexible Error and Random Effects Distributions

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 store of the function bayesBisurvreg or bayesBisurvreg.checkStore
dim	dimension of the response, 1 or 2
prior	a list as required by the argument prior of the function bayesBisurvreg
prior2	a list as required by the argument prior2 of the function bayesBisurvreg
init	a list as required by the argument init of the function bayesBisurvreg
init2	a list as required by the argument init2 of the function bayesBisurvreg
mcmc.par	a list as required by the argument mcmc.par of the function bayesBisurvreg
mcmc.par2	a list as required by the argument mcmc.par2 of the function bayesBisurvreg
design	an object as returned by the function bayessurvreg.design related to either the onset time if doubly censored observations or to the event time. Remark: design$Y contains original times and NOT their logarithmic transformations.
design2	an object as returned by the function bayessurvreg.design related to time-to-event if doubly censored observations. Remark: design2$Y contains original times and NOT their logarithmic transformations.
doubly	logical indicating whether the response is doubly censored or not
prior.beta	a list as required by the argument prior.beta or prior.beta2 of the function bayesBisurvreg
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 bayesBisurvreg.priorInit

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 to n

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 and var.prior containing vectors with the prior mean and prior variance of the beta parameters

Author(s)

Arnošt Komárek [e-mail]

bayesSurv documentation built on Sept. 30, 2024, 9:34 a.m.