rhglmm {hglmm} | R Documentation |
Generic function for simulating data from hidden generalized linear Markov models.
rhglmm(object, ...)
object |
Specification of the model from which to simulate data. |
... |
Other arguments to be passed to methods for this generic function. |
Although the name refers to “generalized linear models”, currently only log linear Poisson models are provided for. More flexibility may be added at a future date.
A data frame with columns equal to the simulated observations and the factors specifying the hidden Markov model.
Rolf Turner r.turner@auckland.ac.nz
See the help for hglmm()
for references.
fitted.hglmm()
rhglmm.default()
rhglmm.hglmm()
bcov()
loc4 <- c("LngRf","BondiE","BondiOff","MlbrOff") SCC4 <- SydColCount[SydColCount$locn %in% loc4,] SCC4$locn <- factor(SCC4$locn) # Get rid of unused levels. rownames(SCC4) <- 1:nrow(SCC4) obj <- list(tpm=matrix(c(0.91,0.09,0.36,0.64),byrow=TRUE,ncol=2), phi=c(-0.34,0.03,-0.32,0.14,-0.05,-0.14,0.10,-1.83), data=SCC4[,1:3]) X <- rhglmm(obj,formula=y~locn+depth+state,cells=c("locn","depth"), miss.frac=0.75,contr="sum")