dprocess workhorse
Evaluates the probability density of a sequence of consecutive state transitions. methods
## S4 method for signature 'pomp' dprocess( object, ..., x = states(object), times = time(object), params = coef(object), log = FALSE )
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.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.dprocess returns a matrix of dimensions nrep x ntimes-1. If d is the returned matrix, d[j,k] is the likelihood (or the log likelihood if log=TRUE) of the transition from state x[,j,k-1] at time times[k-1] to state x[,j,k] at time times[k].
Specification of the process-model density evaluator: dprocess_spec
More on pomp workhorse functions: dinit(), dmeasure(), dprior(), emeasure(), flow(), partrans(), pomp-package, rinit(), rmeasure(), rprior(), rprocess(), skeleton(), vmeasure(), workhorses