dmeasure evaluates the probability density of observations given states.
# S4 method for pomp
dmeasure(
object,
...,
y = obs(object),
x = states(object),
times = time(object),
params = coef(object),
log = FALSE
)dmeasure returns a matrix of dimensions nreps x ntimes.
If d is the returned matrix, d[j,k] is the likelihood (or log likelihood if log = TRUE) of the observation y[,k] at time times[k] given the state x[,j,k].
an object of class ‘pomp’, or of a class that extends ‘pomp’.
This will typically be the output of pomp, simulate, or one of the pomp inference algorithms.
additional arguments are ignored.
a matrix containing observations.
The dimensions of y are nobs x ntimes, where nobs is the number of observables
and ntimes is the length of times.
an array containing states of the unobserved process.
The dimensions of x are nvars x nrep x ntimes,
where nvars is the number of state variables,
nrep is the number of replicates,
and ntimes is the length of times.
One can also pass x as a named numeric vector, which is equivalent to the nrep=1, ntimes=1 case.
a numeric vector (length ntimes) containing times.
These must be in non-decreasing order.
a npar x nrep matrix of parameters.
Each column is treated as an independent parameter set, in correspondence with the corresponding column of x.
if TRUE, log probabilities are returned.
Specification of the measurement density evaluator: dmeasure_spec
More on pomp workhorse functions:
dinit(),
dprior(),
dprocess(),
emeasure(),
flow(),
partrans(),
pomp-package,
rinit(),
rmeasure(),
rprior(),
rprocess(),
skeleton(),
vmeasure(),
workhorses