get.groups {spass} | R Documentation |
Generate Time Series with Negative Binomial Distribution and Multivariate Gamma Frailty with Autoregressive Correlation Structure of Order One with Trend
Description
rnbinom.gf
generates one or more independent time series following the Gamma frailty model. The generated data has negative binomial marginal distribution and the underlying multivariate Gamma frailty an autoregressive covariance structure.
Usage
get.groups(n, size, lambda, rho, tp, trend)
Arguments
n |
number of observations. |
size |
dispersion parameter (the shape parameter of the gamma mixing distribution). Must be strictly positive, need not be integer. |
lambda |
vector of means of trend parameters. |
rho |
correlation coefficient of the underlying autoregressive Gamma frailty. Must be between 0 and 1. |
tp |
number of observed time points. |
trend |
a string giving the trend which is to be simulated. |
Details
The function relies on rnbinom.gf
for creating data with underlying constant or exponential trends.
Value
get.groups
returns a matrix of dimension n
x tp
with marginal negative binomial
distribution with means corresponding to trend parameters lambda
, common dispersion parameter size
and a correlation induce by rho
,
the correlation coefficient of the autoregressive multivariate Gamma frailty.
Source
rnbinom.gf
computes observations from a Gamma frailty model by Fiocco et. al. 2009 using code contributed by Thomas Asendorf.
References
Fiocco M, Putter H, Van Houwelingen JC, (2009), A new serially correlated gamma-frailty process for longitudinal count data Biostatistics Vol. 10, No. 2, pp. 245-257.
See Also
rnbinom.gf
for information on the Gamma frailty model.
Examples
random<-get.groups(n=c(1000,1000), size=c(0.5, 0.5), lambda=c(1, 2), rho=c(0.6, 0.6), tp=7,
trend="constant")
head(random)