bayessurvreg3.help: Helping functions for Bayesian regression with an error...

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

Helping functions for Bayesian regression with an error distribution smoothed using G-splines

Description

These functions are not to be called by ordinary users.

These are just sub-parts of ‘bayessurvreg3’ function to make it more readable for the programmer.

Usage

bayessurvreg3.checkStore(store)

bayessurvreg3.priorInit(prior, init, design, mcmc.par,
   prior2, init2, design2, mcmc.par2, doubly)

bayessurvreg3.priorBeta(prior.beta, init, design)

bayessurvreg3.priorb(prior.b, init, design, mcmc.par)

bayessurvreg3.writeHeaders(dir, doubly, prior.init,
   priorb.di, priorb2.di, store, design, design2,
   version, mclass)

bayessurvreg3.priorinitNb(priorinit.Nb, init, init2,
   design, design2, doubly)

bayessurvreg3.checkrho(rho, doubly)

Arguments

store	a list as required by the argument store of the function bayessurvreg2
prior	a list as required by the argument prior of the function bayessurvreg3
prior2	a list as required by the argument prior2 of the function bayessurvreg3
init	a list as required by the argument init of the function bayessurvreg3
init2	a list as required by the argument init2 of the function bayessurvreg3
mcmc.par	a list as required by the argument mcmc.par of the function bayessurvreg3
mcmc.par2	a list as required by the argument mcmc.par2 of the function bayessurvreg3
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 bayessurvreg3
prior.b	a list as required by the argument prior.b or prior.b2 of the function bayessurvreg3
dir	path to the directory where the sampled values are to be stored
prior.init	a list as returned by the function bayessurvreg3.priorInit
priorb.di	a list as returned by the function bayessurvreg3.priorb
priorb2.di	a list as returned by the function bayessurvreg3.priorb
priorinit.Nb	a list as required by the argument priorinit.Nb of the function bayessurvreg3
rho	a list as required by the argument rho of the function bayessurvreg3
version	it is equal to 3 if either there is no correlation coefficient between the onset and time-to-event random intercepts or this correlation coefficient is fixed to 0 It is equal to 31 if we are estimating correlation coefficient between the onset and time-to-event random intercepts.
mclass	object created by bayessurvreg3 function related to a model which considers possible misclassification of the event status.

Value

Some lists (in most cases).

Value for bayessurvreg3.priorb

A list with the following components:

bparmI

integer arguments for C++ RandomEff constructor

bparmD

double arguments for C++ RandomEff constructor

GsplI

integer arguments for C++ Gspline constructor related to the smothed density of the random intercept

GsplD

double arguments for C++ Gspline constructor related to the smoothed density of the random intercept

specification

1 or 2, one of the G-spline specifications related to the distribution of the random intercept

r

initial component labels (vector of size ncluster) taking values from 1 to the total length of the G-spline related to the random intercept

and the following attributes:

prior.b

init

mcmc.par

Author(s)

Arnošt Komárek [e-mail]

bayesSurv documentation built on Dec. 5, 2022, 5:22 p.m.