probe_match {pomp} | R Documentation |
Probe matching
Description
Estimation of parameters by maximum synthetic likelihood
Usage
## S4 method for signature 'data.frame'
probe_objfun(
data,
est = character(0),
fail.value = NA,
probes,
nsim,
seed = NULL,
params,
rinit,
rprocess,
rmeasure,
partrans,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'pomp'
probe_objfun(
data,
est = character(0),
fail.value = NA,
probes,
nsim,
seed = NULL,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'probed_pomp'
probe_objfun(
data,
est = character(0),
fail.value = NA,
probes,
nsim,
seed = NULL,
...,
verbose = getOption("verbose", FALSE)
)
## S4 method for signature 'probe_match_objfun'
probe_objfun(
data,
est,
fail.value,
seed = NULL,
...,
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.
Internally, |
est |
character vector; the names of parameters to be estimated. |
fail.value |
optional numeric scalar;
if non- |
probes |
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. |
nsim |
the number of model simulations to be computed. |
seed |
integer.
When fitting, it is often best to fix the seed of the random-number generator (RNG).
This is accomplished by setting |
params |
optional; named numeric vector of parameters.
This will be coerced internally to storage mode |
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 |
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 pre-compiled native routine available in a dynamically loaded library.
Setting |
partrans |
optional parameter transformations, constructed using 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 |
... |
additional arguments are passed to |
verbose |
logical; if |
Details
In probe-matching, one attempts to minimize the discrepancy between simulated and actual data, as measured by a set of summary statistics called probes. In pomp, this discrepancy is measured using the “synthetic likelihood” as defined by Wood (2010).
Value
probe_objfun
constructs a stateful objective function for probe matching.
Specifically, probe_objfun
returns an object of class ‘probe_match_objfun’, which is a function suitable for use in an optim
-like optimizer.
In particular, this function takes a single numeric-vector argument that is assumed to contain the parameters named in est
, in that order.
When called, it will return the negative synthetic log likelihood for the probes specified.
It is a stateful function:
Each time it is called, it will remember the values of the parameters and its estimate of the synthetic likelihood.
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.
Important Note
Since pomp cannot guarantee that the final call an optimizer makes to the function is a call at the optimum, it cannot guarantee that the parameters stored in the function are the optimal ones. Therefore, it is a good idea to evaluate the function on the parameters returned by the optimization routine, which will ensure that these parameters are stored.
Warning! Objective functions based on C snippets
If you use C snippets (see Csnippet
), a dynamically loadable library will be built.
As a rule, pomp functions load this library as needed and unload it when it is no longer needed.
The stateful objective functions are an exception to this rule.
For efficiency, calls to the objective function do not execute pompLoad
or pompUnload
:
rather, it is assumed that pompLoad
has been called before any call to the objective function.
When a stateful objective function using one or more C snippets is created, pompLoad
is called internally to build and load the library:
therefore, within a single R session, if one creates a stateful objective function, one can freely call that objective function and (more to the point) pass it to an optimizer that calls it freely, without needing to call pompLoad
.
On the other hand, if one retrieves a stored objective function from a file, or passes one to another R session, one must call pompLoad
before using it.
Failure to do this will typically result in a segmentation fault (i.e., it will crash the R session).
Author(s)
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 methods based on summary statistics:
abc()
,
basic_probes
,
nlf
,
probe()
,
spect()
,
spect_match
More on pomp estimation algorithms:
abc()
,
bsmc2()
,
estimation_algorithms
,
mif2()
,
nlf
,
pmcmc()
,
pomp-package
,
spect_match
More on maximization-based estimation methods:
mif2()
,
nlf
,
spect_match
,
traj_match
Examples
gompertz() -> po
## A list of probes:
plist <- list(
mean=probe_mean("Y",trim=0.1,transform=sqrt),
sd=probe_sd("Y",transform=sqrt),
probe_marginal("Y",ref=obs(po)),
probe_acf("Y",lags=c(1,3,5),type="correlation",transform=sqrt),
probe_quantile("Y",prob=c(0.25,0.75),na.rm=TRUE)
)
## Construct the probe-matching objective function.
## Here, we just want to estimate 'K'.
po |>
probe_objfun(probes=plist,nsim=100,seed=5069977,
est="K") -> f
## Any numerical optimizer can be used to minimize 'f'.
if (require(subplex)) {
subplex(fn=f,par=0.4,control=list(reltol=1e-5)) -> out
} else {
optim(fn=f,par=0.4,control=list(reltol=1e-5)) -> out
}
## Call the objective one last time on the optimal parameters:
f(out$par)
coef(f)
## There are 'plot' and 'summary' methods:
f |> as("probed_pomp") |> plot()
f |> summary()
## One can convert an objective function to a data frame:
f |> as("data.frame") |> head()
f |> as("probed_pomp") |> as("data.frame") |> head()
f |> probe() |> plot()
## One can modify the objective function with another call
## to 'probe_objfun':
f |> probe_objfun(est=c("r","K")) -> f1
optim(fn=f1,par=c(0.3,0.3),control=list(reltol=1e-5)) -> out
f1(out$par)
coef(f1)