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.
1 2 3  bayessurvreg.design(m, formula, random, data, transform, dtransform)
bayessurvreg.checknsimul(nsimul)

m, formula, random, data, transform, dtransform 

nsimul 
a list 
Some lists.
A~list with the following components:
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
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
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
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
number of columns in the design matrix X. Note that the matrix X contains covariates for both fixed and random effects
number of fixed effects involved in the model. Note that possible intercept is always removed from the model
number of random effects in the model, possible random intercept included
TRUE
/FALSE
indicating whether the
random intercept is included in the model
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).
design matrix containing covariates
response matrix extracted from formula
using model.extract
design matrix extracted from formula
using
model.matrix
function
a~vector of length n
with identifications of
clusters (as given by cluster
in formula
)
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)
row names of Xinit
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)"
???
number of factor covariates in the model formula
???
Arnošt Komárek arnost.komarek[AT]mff.cuni.cz
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