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

Description Usage Arguments Value See Also

Evaluates the deterministic skeleton at a point or points in state space, given parameters. In the case of a discrete-time system, the skeleton is a map. In the case of a continuous-time system, the skeleton is a vectorfield. NB: skeleton just evaluates the deterministic skeleton; it does not iterate or integrate (see trajectory for this).

1 2	## S4 method for signature 'pomp' skeleton(object, x, times, params, ...)

`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.

skeleton returns an array of dimensions nvar x nrep x ntimes. If f is the returned matrix, f[i,j,k] is the i-th component of the deterministic skeleton at time times[k] given the state x[,j,k] and parameters params[,j].

Specification of the deterministic skeleton: skeleton_spec

Other pomp workhorses: dmeasure, dprior, dprocess, partrans, rinit, rmeasure, rprior, rprocess, workhorses