bayessurvreg.help {bayesSurv} | R Documentation |
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\times \mbox{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 matrixX
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
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 thej
th column of matrixX
is a fixed effects. it is equal tol
if thej
th column of matrixX
corresponds to thel
th 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@mff.cuni.cz