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*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 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
right-censored observations, 2 for left-censored observations, 3 for
interval-censored observations).
- X
- design matrix containing covariates
- Yinit
- response matrix extracted from
formula
using model.extract
- Xinit
- design matrix extracted from
formula
using
model.matrix
function
- cluster
- a~vector of length
n
with identifications of
clusters (as given by cluster
in formula
)
- 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
[Package
bayesSurv version 0.6-1
Index]