dmeasure function

dmeasure workhorse

dmeasure evaluates the probability density of observations given states. methods

## S4 method for signature 'pomp'
dmeasure(
  object,
  ...,
  y = obs(object),
  x = states(object),
  times = time(object),
  params = coef(object),
  log = FALSE
)

Arguments

• object: 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.
• y: 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.
• x: 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.
• times: a numeric vector (length ntimes) containing times. These must be in non-decreasing order.
• params: a npar x nrep matrix of parameters. Each column is treated as an independent parameter set, in correspondence with the corresponding column of x.
• log: if TRUE, log probabilities are returned.

Returns

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].

See Also

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