probe {pomp}  R Documentation 
Probes (AKA summary statistics)
Description
Probe a partiallyobserved Markov process by computing summary statistics and the synthetic likelihood.
Usage
## S4 method for signature 'data.frame'
probe(
data,
probes,
nsim,
seed = NULL,
params,
rinit,
rprocess,
rmeasure,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'pomp'
probe(
data,
probes,
nsim,
seed = NULL,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'probed_pomp'
probe(
data,
probes,
nsim,
seed = NULL,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'probe_match_objfun'
probe(data, seed, ..., verbose = getOption("verbose", FALSE))
## S4 method for signature 'objfun'
probe(data, seed = NULL, ...)
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.
Internally, 
probes 
a single probe or a list of one or more probes. A probe is simply a scalar or vectorvalued function of one argument that can be applied to the data array of a ‘pomp’. A vectorvalued probe must always return a vector of the same size. A number of useful probes are provided with the package: see basic probes. 
nsim 
the number of model simulations to be computed. 
seed 
optional integer;
if set, the pseudorandom number generator (RNG) will be initialized with 
params 
optional; named numeric vector of parameters.
This will be coerced internally to storage mode 
rinit 
simulator of the initialstate distribution.
This can be furnished either as a C snippet, an R function, or the name of a precompiled native routine available in a dynamically loaded library.
Setting 
rprocess 
simulator of the latent state process, specified using one of the rprocess plugins.
Setting 
rmeasure 
simulator of the measurement model, specified either as a C snippet, an R function, or the name of a precompiled native routine available in a dynamically loaded library.
Setting 
... 
additional arguments are passed to 
verbose 
logical; if 
Details
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 “probematching”, a
generalized methodofmoments 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 probematching objective function (a ‘probe_match_objfun’ object), by default, the
randomnumber generator seed is fixed at the value given when the objective function was constructed.
Specifying NULL
or an integer for seed
overrides this behavior.
Value
probe
returns an object of class ‘probed_pomp’, which contains the data and the model, together with the results of the probe
calculation.
Methods
The following methods are available.
plot
displays diagnostic plots.
summary
displays summary information. The summary includes quantiles (fractions of simulations with probe values less than those realized on the data) and the corresponding twosided pvalues. In addition, the “synthetic likelihood” (Wood 2010) is computed, under the assumption that the probe values are multivariatenormally distributed.
logLik
returns the synthetic likelihood for the probes. NB: in general, this is not the same as the likelihood.
as.data.frame

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.
Note for Windows users
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 to cause the C snippets to be written to a file of your choice, thus avoiding the use of temporary files altogether.
Author(s)
Daniel C. Reuman, Aaron A. King
References
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. doi:10.2307/176658.
S. N. Wood Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466, 1102–1104, 2010. doi:10.1038/nature09319.
See Also
More on pomp elementary algorithms:
elementary_algorithms
,
kalman
,
pfilter()
,
pomppackage
,
simulate()
,
spect()
,
trajectory()
,
wpfilter()
More on methods based on summary statistics:
abc()
,
basic_probes
,
nlf
,
probe_match
,
spect()
,
spect_match