| sim.hmm {hmm.discnp} | R Documentation |
Simulates one or more replicates of discrete data
from a model such as is fitted by the function hmm().
sim.hmm(nsim, tpm, Rho, nrep=1)
nsim |
The length of each sequence of observations. |
tpm |
The transition probability matrix for the underlying hidden Markov
chain(s). Note that the rows of tpm must sum to 1.
Ignored if ncol(Rho)==1.
|
Rho |
A matrix specifying the probability of an observation taking on one
of a set of possible values, given the state of the underlying hidden
Markov chain. Note that the columns of Rho must sum to 1.
If ncol(Rho)==1 the data are i.i.d. from the distribution
specified by the single column of Rho.
|
nrep |
The number of independent replicate sequences of observations. |
A matrix with nsim rows and nrep columns; if
nrep==1, then the returned value is a vector of length
nsim. Each column of the matrix forms an independent
replicate of observations from the specified hidden Markov model.
The values of those observations are integers between 1 and
nrow(Rho).
Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
hmm()
P <- matrix(c(0.7,0.3,0.1,0.9),2,2,byrow=TRUE)
R <- matrix(c(0.5,0,0.1,0.1,0.3,
0.1,0.1,0,0.3,0.5),5,2)
set.seed(42)
y.sim <- sim.hmm(300,P,R,20)