phmm.design {phmm}R Documentation

PHMM Design

Description

Internal function for extracting design matrix from call to phmm. Code adapted from bayesurvreg.design function in the bayesSurv package.

Usage

phmm.design(m, formula, random, data)

Arguments

m match.call from call to phmm.
formula formula component from call to phmm.
random random component from call to phmm.
data data component from call to phmm.

Value

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
nZ number of columns in the design matrix Z. Note that the matrix Z 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).
Z design matrix containing covariates for fixed effects.
W design matrix containing covariates for random effects.
Yinit response matrix extracted from formula using model.extract
Zinit 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 nZ identifying fixed and random effects. indb[j] = -1 if the jth column of matrix Z is a fixed effects. it is equal to l if the jth column of matrix Z corresponds to the lth random effect (in C++ indexing)
rnames.Z row names of Zinit
names.random column names of the Z 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)"

References

Arnost Komarek (2007). bayesSurv: Bayesian Survival Regression with Flexible Error and Random Effects Distributions. R package version 0.5-9. http://www.karlin.mff.cuni.cz/~komarek

See Also

phmm, phmm.


[Package phmm version 0.5 Index]