dmeasure: dmeasure workhorse

In pomp: Statistical Inference for Partially Observed Markov Processes

dmeasure workhorse

Description

dmeasure evaluates the probability density of observations given states.

Usage

## 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.
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.
...	additional arguments are ignored.
log	if TRUE, log probabilities are returned.

Value

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

pomp documentation built on Aug. 8, 2023, 1:08 a.m.