dprocess: dprocess workhorse

In pomp: Statistical Inference for Partially Observed Markov Processes

dprocess workhorse

Description

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

Usage

## S4 method for signature 'pomp'
dprocess(
  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.
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.

Value

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].

See Also

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

pomp documentation built on Sept. 13, 2024, 1:08 a.m.