Specification of rmeasure
The default rmeasure is undefined.
It will yield missing values (NA).
The measurement model is the link between the data and the unobserved state process.
It can be specified either by using one or both of the rmeasure and dmeasure arguments.
Suppose you have a procedure to simulate observations given the value of the latent state variables. Then you can furnish
rmeasure = f
to pomp algorithms,
where f is a C snippet or R function that implements your procedure.
Using a C snippet is much preferred, due to its much greater computational efficiency.
See Csnippet for general rules on writing C snippets.
In writing an rmeasure C snippet, bear in mind that:
The goal of such a snippet is to fill the observables with random values drawn from the measurement model distribution. Accordingly, each observable should be assigned a new value.
In addition to the states, parameters, covariates (if any), and observables, the variable t, containing the time of the observation, will be defined in the context in which the snippet is executed.
The demos and the tutorials on the package website give examples as well.
It is also possible, though far less efficient, to specify rmeasure using an R function.
In this case, specify the measurement model simulator by furnishing
rmeasure = f
to pomp, where f is an R function.
The arguments of f should be chosen from among the state variables, parameters, covariates, and time.
It must also have the argument ....
f must return a named numeric vector of length equal to the number of observable variables.
Other information on model implementation: Csnippet,
accumulators,
covariate_table,
distributions, dmeasure_spec,
dprocess_spec,
parameter_trans,
pomp-package, prior_spec,
rinit_spec, rprocess_spec,
skeleton_spec,
transformations, userdata