Helping function for Bayesian survival regression models.
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
Value for bayessurvreg.design
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
nclusterwith 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
FALSEindicating 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
nwith identifications of clusters (as given by
a~vector of length
nXidentifying fixed and random effects.
indb[j] = -1if 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