pomp: Constructor of the basic pomp object

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

Description

This function constructs a ‘pomp’ object, encoding a partially-observed Markov process (POMP) model together with a uni- or multi-variate time series. As such, it is central to all the package's functionality. One implements the POMP model by specifying some or all of its basic components. These comprise:

rinit,

which samples from the distribution of the state process at the zero-time;

rprocess,

the simulator of the unobserved Markov state process;

dprocess,

the evaluator of the probability density function for transitions of the unobserved Markov state process;

rmeasure,

the simulator of the observed process, conditional on the unobserved state;

dmeasure,

the evaluator of the measurement model probability density function;

rprior,

which samples from a prior probability distribution on the parameters;

dprior,

which evaluates the prior probability density function;

skeleton,

which computes the deterministic skeleton of the unobserved state process;

partrans,

which performs parameter transformations.

The basic structure and its rationale are described in the Journal of Statistical Software paper, an updated version of which is to be found on the package website.

Usage

pomp(data, times, t0, ..., rinit, rprocess, dprocess, rmeasure, dmeasure,
  skeleton, rprior, dprior, partrans, covar, params, accumvars, obsnames,
  statenames, paramnames, covarnames, PACKAGE, globals, cdir, cfile,
  shlib.args, compile = TRUE, verbose = getOption("verbose", FALSE))

Arguments

data	either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation.
times	the times at which observations are made. times must indicate the column of observation times by name or index. The time vector must be numeric and non-decreasing. Internally, data will be internally coerced to an array with storage-mode double.
t0	The zero-time, i.e., the time of the initial state. This must be no later than the time of the first observation, i.e., t0 <= times[1].
...	additional arguments supply new or modify existing model characteristics or components. See pomp for a full list of recognized arguments. When named arguments not recognized by pomp are provided, these are made available to all basic components via the so-called userdata facility. This allows the user to pass information to the basic components outside of the usual routes of covariates (covar) and model parameters (params). See the userdata documentation here for information on how to use this facility.
rinit	simulator of the initial-state distribution. This can be furnished either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting rinit=NULL sets the initial-state simulator to its default. For more information, see here.
rprocess	simulator of the latent state process, specified using one of the rprocess plugins. Setting rprocess=NULL removes the latent-state simulator. For more information, see the documentation on these plugins.
dprocess	optional; specification of the probability density evaluation function of the unobserved state process. Setting dprocess=NULL removes the latent-state density evaluator. For more information, see here.
rmeasure	simulator of the measurement model, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting rmeasure=NULL removes the measurement model simulator. For more information, see here.
dmeasure	evaluator of the measurement model density, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. Setting dmeasure=NULL removes the measurement density evaluator. For more information, see here.
skeleton	optional; the deterministic skeleton of the unobserved state process. Depending on whether the model operates in continuous or discrete time, this is either a vectorfield or a map. Accordingly, this is supplied using either the vectorfield or map fnctions. For more information, see here. Setting skeleton=NULL removes the deterministic skeleton.
rprior	optional; prior distribution sampler, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. For more information, see here. Setting rprior=NULL removes the prior distribution sampler.
dprior	optional; prior distribution density evaluator, specified either as a C snippet, an R function, or the name of a pre-compiled native routine available in a dynamically loaded library. For more information, see here. Setting dprior=NULL resets the prior distribution to its default, which is a flat improper prior.
partrans	optional parameter transformations, constructed using parameter_trans. Many algorithms for parameter estimation search an unconstrained space of parameters. When working with such an algorithm and a model for which the parameters are constrained, it can be useful to transform parameters. One should supply the partrans argument via a call to parameter_trans. For more information, see here. Setting partrans=NULL removes the parameter transformations, i.e., sets them to the identity transformation.
covar	optional covariate table, constructed using covariate_table. If a covariate table is supplied, then the value of each of the covariates is interpolated as needed. The resulting interpolated values are made available to the appropriate basic components. See the documentation for covariate_table for details.
params	optional; named numeric vector of parameters. This will be coerced internally to storage mode double.
accumvars	optional character vector; contains the names of accumulator variables. See here for a definition and discussion of accumulator variables.
obsnames	optional character vector; names of the observables. It is not usually necessary to specify obsnames since, by default, these are read from the names of the data variables.
statenames	optional character vector; names of the latent state variables. It is typically only necessary to supply statenames when C snippets are in use.
paramnames	optional character vector; names of model parameters. It is typically only necessary to supply paramnames when C snippets are in use.
covarnames	optional character vector; names of the covariates. It is not usually necessary to specify covarnames since, by default, these are read from the names of the covariates.
PACKAGE	optional character; the name (without extension) of the external, dynamically loaded library in which any native routines are to be found. This is only useful if one or more of the model components has been specified using a precompiled dynamically loaded library; it is not used for any component specified using C snippets. PACKAGE can name at most one library.
globals	optional character; arbitrary C code that will be hard-coded into the shared-object library created when C snippets are provided. If no C snippets are used, globals has no effect.
cdir	optional character variables. cdir specifies the name of the directory within which C snippet code will be compiled. By default, this is in a temporary directory specific to the R session. cfile gives the name of the file (in directory cdir) into which C snippet codes will be written. By default, a random filename is used.
cfile	optional character variables. cdir specifies the name of the directory within which C snippet code will be compiled. By default, this is in a temporary directory specific to the R session. cfile gives the name of the file (in directory cdir) into which C snippet codes will be written. By default, a random filename is used.
shlib.args	optional character variables. Command-line arguments to the R CMD SHLIB call that compiles the C snippets.
compile	logical; if FALSE, compilation of the C snippets will be postponed until they are needed.
verbose	logical; if TRUE, diagnostic messages will be printed to the console.

Details

Each basic component is supplied via an argument of the same name. These can be given in the call to pomp, or to many of the package's other functions. In any case, the effect is the same: to add, remove, or modify the basic component.

Each basic component can be furnished using C snippets, R functions, or pre-compiled native routine available in user-provided dynamically loaded libraries.

Value

The pomp constructor function returns an object, call it P, of class ‘pomp’. P contains, in addition to the data, any elements of the model that have been specified as arguments to the pomp constructor function. One can add or modify elements of P by means of further calls to pomp, using P as the first argument in such calls. One can pass P to most of the pomp package methods via their data argument.

Note

It is not typically necessary (or indeed often feasible) to define all of the basic components for any given purpose. Each pomp algorithm makes use of only a subset of these components. Any algorithm requiring a component that is not present will generate an error letting you know that you have not provided a needed component. FIXME

Author(s)

Aaron A. King

References

A. A. King, D. Nguyen, and E. L. Ionides (2016) Statistical Inference for Partially Observed Markov Processes via the Package pomp. Journal of Statistical Software 69(12): 1–43.

kidusasfaw/pomp documentation built on May 20, 2019, 2:59 p.m.