Description Usage Arguments Details Value References
This function constructs a class ‘spatPomp’ object, encoding a spatiotemporal partially observed Markov process (SpatPOMP) model together with a uni or multivariate time series on a collection of units.
Users will typically develop a POMP model for a single unit before embarking on a coupled SpatPOMP analysis.
Consequently, we assume some familiarity with pomp and its description by King, Nguyen and Ionides (2016).
The spatPomp
class inherits from pomp
with the additional unit structure being a defining feature of the resulting models and inference algorithms.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  spatPomp(
data,
units,
times,
covar,
t0,
...,
eunit_measure,
munit_measure,
vunit_measure,
dunit_measure,
runit_measure,
rprocess,
rmeasure,
dprocess,
dmeasure,
skeleton,
rinit,
rprior,
dprior,
unit_statenames,
unit_accumvars,
shared_covarnames,
globals,
paramnames,
params,
cdir,
cfile,
shlib.args,
PACKAGE,
partrans,
compile = TRUE,
verbose = getOption("verbose", FALSE)
)

data 
either a dataframe holding the spatiotemporal data,
or an object of class ‘spatPomp’, i.e., the output of another spatPomp calculation.
If dataframe, the user must provide the name of the times column using the 
units 
when 
times 
the sequence of observation times.

covar 
An optional dataframe for supplying covariate information. If provided, there must be two
columns that provide the observation time and the observation spatial unit with the same names and arrangement as the 
t0 
The zerotime, i.e., the time of the initial state.
This must be no later than the time of the first observation, i.e., 
... 
If there are arguments that the user would like to pass to pomp's basic constructor function's ... argument, this argument passes them along. Not recommended for this version of spatPomp. 
eunit_measure 
Evaluator of the expected measurement given the latent states and model parameters. The 
munit_measure 
Evaluator of a momentmatched parameter set (like the standard deviation parameter of a normal distribution or the size parameter of a negative binomial distribution) given an empirical variance estimate, the latent states and all model parameters.
Only Csnippets are accepted. The Csnippet should assign the scalar approximation to the measurement variance parameter to the predefined variable corresponding to that parameter, which has been predefined with a 
vunit_measure 
Evaluator of the theoretical measurement variance given the latent states and model parameters. The 
dunit_measure 
Evaluator of the unit measurement model density given the measurement, the latent states and model parameters. The 
runit_measure 
Simulator of the unit measurement model given the latent states and the model parameters.
The 
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 
dprocess 
optional;
specification of the probability density evaluation function of the unobserved state process.
Setting 
dmeasure 
evaluator of the measurement model density, specified either as a C snippet, an R function, or the name of a precompiled native routine available in a dynamically loaded library.
Setting 
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 
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 
rprior 
optional; prior distribution sampler, specified either as a C snippet, an R function, or the name of a precompiled native routine available in a dynamically loaded library.
For more information, see prior specification.
Setting 
dprior 
optional; prior distribution density evaluator, specified either as a C snippet, an R function, or the name of a precompiled native routine available in a dynamically loaded library.
For more information, see prior specification.
Setting 
unit_statenames 
The names of the components of the latent state. E.g. if the user is constructing an joint SIR model
over many spatial units, 
unit_accumvars 
a subset of the 
shared_covarnames 
If 
globals 
optional character;
arbitrary C code that will be hardcoded into the sharedobject library created when C snippets are provided.
If no C snippets are used, 
paramnames 
optional character vector;
names of model parameters.
It is typically only necessary to supply 
params 
optional; named numeric vector of parameters.
This will be coerced internally to storage mode 
cdir 
optional character variable.

cfile 
optional character variable.

shlib.args 
optional character variables.
Commandline arguments to the 
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.

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 
compile 
logical;
if 
verbose 
logical; if 
One implements a SpatPOMP model by specifying some or all of its basic components, including:
the simulator from the distribution of the latent state process at the zerotime;
the transition simulator of the latent state process;
the evaluator of the conditional density at a unit's measurement given the unit's latent state;
the evaluator of the expectation of a unit's measurement given the unit's latent state;
the evaluator of the momentmatched parameter set given a unit's latent state and some empirical measurement variance;
the evaluator of the variance of a unit's measurement given the unit's latent state;
the simulator of a unit's measurement conditional on the unit's latent state;
the evaluator of the density for transitions of the latent state process;
the simulator of the measurements conditional on the latent state;
the evaluator of the conditional density of the measurements given the latent state;
the simulator from a prior distribution on the parameters;
the evaluator of the prior density;
which computes the deterministic skeleton of the unobserved state process;
which performs parameter transformations.
The basic structure and its rationale are described in Asfaw et al. (2020).
Each basic component is supplied via an argument of the same name to spatPomp()
.
The five unitlevel model components must be provided via C snippets. The remaining components, whose behaviors are inherited from
pomp may be furnished using C snippets, R functions, or precompiled native routine available in userprovided dynamically loaded libraries.
An object of class ‘spatPomp’ representing observations and model components from the spatiotemporal POMP model.
2020
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