|basic components||R Documentation|
Mathematically, the parts of a POMP model include the latent-state process transition distribution, the measurement-process distribution, the initial-state distribution, and possibly a prior parameter distribution. Algorithmically, each of these corresponds to at least two distinct operations. In particular, for each of the above parts, one sometimes needs to make a random draw from the distribution and sometimes to evaluate the density function. Accordingly, for each such component, there are two basic model components, one prefixed by a ‘r’, the other by a ‘d’, following the usual R convention.
In addition to the parts listed above, pomp includes two additional basic model components: the deterministic skeleton, and parameter transformations that can be used to map the parameter space onto a Euclidean space for estimation purposes.
There are thus altogether eleven basic model components:
rprocess, which samples from the latent-state transition distribution,
dprocess, which evaluates the latent-state transition density,
rmeasure, which samples from the measurement distribution,
emeasure, which computes the conditional expectation of the measurements, given the latent states,
vmeasure, which computes the conditional covariance matrix of the measurements, given the latent states,
dmeasure, which evaluates the measurement density,
rprior, which samples from the prior distribution,
dprior, which evaluates the prior density,
rinit, which samples from the initial-state distribution,
skeleton, which evaluates the deterministic skeleton,
partrans, which evaluates the forward or inverse parameter transformations.
Each of these can be set or modified in the
pomp constructor function or in any of the pomp elementary algorithms or estimation algorithms using an argument that matches the basic model component.
A basic model component can be unset by passing
NULL in the same way.
Help pages detailing each basic model component are provided.
workhorse functions, elementary algorithms, estimation algorithms.
More on implementing POMP models:
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