Probe a partially-observed Markov process by computing summary statistics and the synthetic likelihood.
# S4 method for data.frame
probe(
data,
probes,
nsim,
seed = NULL,
params,
rinit,
rprocess,
rmeasure,
...,
verbose = getOption("verbose", FALSE)
)# S4 method for pomp
probe(
data,
probes,
nsim,
seed = NULL,
...,
verbose = getOption("verbose", FALSE)
)
# S4 method for probed_pomp
probe(
data,
probes,
nsim,
seed = NULL,
...,
verbose = getOption("verbose", FALSE)
)
# S4 method for probe_match_objfun
probe(data, seed, ..., verbose = getOption("verbose", FALSE))
# S4 method for objfun
probe(data, seed = NULL, ...)
either a data frame holding the time series data, or an object of class ‘pomp’, i.e., the output of another pomp calculation.
a single probe or a list of one or more probes. A probe is simply a scalar- or vector-valued function of one argument that can be applied to the data array of a ‘pomp’. A vector-valued probe must always return a vector of the same size. A number of useful probes are provided with the package: see basic probes.
the number of model simulations to be computed.
optional integer;
if non-NULL, the random number generator will be initialized with this seed for simulations.
See simulate.
optional; named numeric vector of parameters.
This will be coerced internally to storage mode double.
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 ?rinit_spec.
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 ?rprocess_spec for the documentation on these plugins.
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 ?rmeasure_spec.
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 ?userdata for information on how to use this facility.
logical; if TRUE, diagnostic messages will be printed to the console.
probe returns an object of class ‘probed_pomp’, which contains the data and the model, together with the results of the probe calculation.
The following methods are available.
displays diagnostic plots.
displays summary information. The summary includes quantiles (fractions of simulations with probe values less than those realized on the data) and the corresponding two-sided p-values. In addition, the “synthetic likelihood” (Wood 2010) is computed, under the assumption that the probe values are multivariate-normally distributed.
returns the synthetic likelihood for the probes. NB: in general, this is not the same as the likelihood.
coerces a ‘probed_pomp’ to a ‘data.frame’.
The latter contains the realized values of the probes on the data and on the simulations.
The variable .id indicates whether the probes are from the data or simulations.
Some Windows users report problems when using C snippets in parallel computations.
These appear to arise when the temporary files created during the C snippet compilation process are not handled properly by the operating system.
To circumvent this problem, use the cdir and cfile options (described here) to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.
probe applies one or more “probes” to time series data and
model simulations and compares the results. It can be used to diagnose
goodness of fit and/or as the basis for “probe-matching”, a
generalized method-of-moments approach to parameter estimation.
A call to probe results in the evaluation of the probe(s) in
probes on the data. Additionally, nsim simulated data sets
are generated (via a call to simulate) and
the probe(s) are applied to each of these. The results of the probe
computations on real and simulated data are stored in an object of class
‘probed_pomp’.
When probe operates on a probe-matching objective function (a ‘probe_match_objfun’ object), by default, the
random-number generator seed is fixed at the value given when the objective function was constructed.
Specifying NULL or an integer for seed overrides this behavior.
B.E. Kendall, C.J. Briggs, W.W. Murdoch, P. Turchin, S.P. Ellner, E. McCauley, R.M. Nisbet, and S.N. Wood. Why do populations cycle? A synthesis of statistical and mechanistic modeling approaches. Ecology 80, 1789--1805, 1999.
S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466, 1102--1104, 2010.
More on pomp elementary algorithms:
elementary_algorithms,
pfilter(),
pomp-package,
simulate(),
spect(),
trajectory(),
wpfilter()
More on pomp methods based on summary statistics:
abc(),
basic_probes,
probe.match,
spect()