bayessurvreg.help: Helping function for Bayesian survival regression models.

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

Helping function for Bayesian survival regression models.

Description

These functions are not to be called by ordinary users.

These are just sub-parts of ‘bayessurvreg’ functions to make them more readable for the programmer.

Usage

bayessurvreg.design(m, formula, random, data, transform, dtransform)

bayessurvreg.checknsimul(nsimul)

Arguments

m, formula, random, data, transform, dtransform
nsimul	a list

Value

Some lists.

Value for bayessurvreg.design

A~list with the following components:

n

number of observations (in the case of bivariate data, this is a~number of single observations, i.e. 2*sample size) included in the dataset

ncluster

number of clusters included in the dataset. In the case of bivariate data this is equal to the number of bivariate observations. If there are no random effects included in the model and if the observations are not bivariate then ncluster = n

nwithin

a~vector of length equal to ncluster with numbers of observations within each cluster. In the case of bivariate observations this is a~vector filled with 2's, if there are no random effects and if the observations are not bivariate then this is a~vector filled with 1's

nY

number of columns in the response matrix Y. This is equal to 2 if there are no interval-censored observations and equal to 3 if there is at least one interval censored observation in the dataset

nX

number of columns in the design matrix X. Note that the matrix X contains covariates for both fixed and random effects

nfixed

number of fixed effects involved in the model. Note that possible intercept is always removed from the model

nrandom

number of random effects in the model, possible random intercept included

randomInt

TRUE/FALSE indicating whether the random intercept is included in the model

Y

response matrix. Its last column is always equal to the status indicator (1 for exactly observed event times, 0 for right-censored observations, 2 for left-censored observations, 3 for interval-censored observations).

X

design matrix containing covariates

Yinit

response matrix extracted from formula using model.extract

Xinit

design matrix extracted from formula using model.matrix function

cluster

a~vector of length n with identifications of clusters (as given by cluster in formula)

indb

a~vector of length nX identifying fixed and random effects. indb[j] = -1 if the jth column of matrix X is a fixed effects. it is equal to l if the jth column of matrix X corresponds to the lth random effect (in C++ indexing)

rnames.X

row names of Xinit

names.random

column names of the X matrix corespning to the random effects. If there is the random intercept in the model, the first component of this vector is equal to "(Intercept)"

factors

???

n.factors

number of factor covariates in the model formula

n.in.factors

???

Author(s)

Arnošt Komárek [e-mail]

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