dprocess: dprocess
In kidusasfaw/pomp: Statistical Inference for Partially Observed Markov Processes

Description Usage Arguments Value See Also

Evaluates the probability density of a sequence of consecutive state transitions.

1 2	## S4 method for signature 'pomp' dprocess(object, x, times, params, ..., 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.
`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`.
`...`	additional arguments are ignored.
`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].