Helping function for Bayesian survival regression models.
Description
These functions are not to be called by ordinary users.
These are just subparts of ‘bayessurvreg’ functions to make them more readable for the programmer.
Usage
1 2 3  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 intervalcensored 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 rightcensored observations, 2 for leftcensored observations, 3 for intervalcensored observations).
 X
design matrix containing covariates
 Yinit
response matrix extracted from
formula
usingmodel.extract
 Xinit
design matrix extracted from
formula
usingmodel.matrix
function cluster
a~vector of length
n
with identifications of clusters (as given bycluster
informula
) 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 arnost.komarek[AT]mff.cuni.cz