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.
1 2 3
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
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 interval-censored 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
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 right-censored observations, 2 for left-censored observations, 3 for interval-censored observations).
design matrix containing covariates
response matrix extracted from
design matrix extracted from
a~vector of length
n with identifications of
clusters (as given by
a~vector of length
nX identifying fixed and random
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
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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