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)

### Arguments

`m, formula, random, data, transform, dtransform` |

### 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)

Arnost Komarek arnost.komarek@med.kuleuven.be

[Package bayesSurv version 0.4 Index]