Description Usage Arguments Details Value Author(s) See Also
The functions documented here can be used to specify the rprocess
and dprocess
slots for a pomp
model.
There are options for discrete- and continuous-time Markov processes.
1 2 3 4 5 | onestep.sim(step.fun, PACKAGE)
euler.sim(step.fun, delta.t, PACKAGE)
discrete.time.sim(step.fun, delta.t = 1, PACKAGE)
gillespie.sim(rate.fun, v, d, PACKAGE)
onestep.dens(dens.fun, PACKAGE)
|
step.fun |
This can be either an R function, the name of a compiled, dynamically loaded native function containing the model simulator, or a For an explanation and examples on the use of If it is an R function, it should have prototype step.fun(x,t,params,delta.t,...). Here, If pomp_onestep_sim as defined in the header file ‘pomp.h’, which is included with the pomp package. Do file.show(system.file("include/pomp.h",package="pomp")) to view this header file. For details on how to write such codes, see Details. |
rate.fun |
This can be either an R function, a For examples on the use of If pomp_ssa_rate_fn as defined in the header ‘pomp.h’, which is included with the package. For details on how to write such codes, see Details. |
v, d |
Matrices that specify the continuous-time Markov process in terms of its elementary events.
Each should have dimensions |
dens.fun |
This can be either an R function, a If it is an R function, it should be of the form dens.fun(x1,x2,t1,t2,params,...). Here, If pomp_onestep_pdf as defined in the header ‘pomp.h’, which is included with the pomp package.
This function should return the log likelihood of a transition from |
delta.t |
Size of Euler time-steps. |
PACKAGE |
an optional argument that specifies to which dynamically loaded library we restrict the search for the native routines.
If this is “base”, we search in the R executable itself.
This argument is ignored if |
onestep.sim
is the appropriate choice when it is possible to simulate the change in state from one time to another, regardless of how large the interval between them is.
To use onestep.sim
, you must write a function step.fun
that will advance the state process from one arbitrary time to another.
euler.sim
is appropriate when one cannot do this but can compute the change in state via a sequence of smaller steps.
This is desirable, for example, if one is simulating a continuous time process but is willing to approximate it using an Euler approach.
discrete.time.sim
is appropriate when the process evolves in discrete time.
To use euler.sim
or discrete.time.sim
, you must write a function step.fun
that will take a single Euler step, of size at most delta.t
.
The functions euler.sim
and discrete.time.sim
will create simulators that take as many steps as needed to get from one time to another.
See below for information on how euler.sim
chooses the actual step size it uses.
gillespie.sim
allows exact simulation of a continuous-time, discrete-state Markov process using Gillespie's algorithm.
This is an “event-driven” approach: correspondingly, to use gillespie.sim
, you must write a function rate.fun
that computes the rates of each elementary kind of event and specify two matrices (d,v
) that describe, respectively, the dependencies of each rate and the consequences of each event.
onestep.dens
will generate a suitable dprocess
function when one can compute the likelihood of a given state transition simply by knowing the states at two times under the assumption that the state has not changed between the times.
This is typically possible, for instance, when the rprocess
function is implemented using onestep.sim
, euler.sim
, or discrete.time.sim
.
[NB: currently, there are no high-level algorithms in pomp that use dprocess
.
This function is provided for completeness only, with an eye toward future development.]
If step.fun
is written as an R function, it must have at least the arguments x
, t
, params
, delta.t
, and ...
.
On a call to this function, x
will be a named vector of state variables, t
a scalar time, and params
a named vector of parameters.
The length of the Euler step will be delta.t
.
If the argument covars
is included and a covariate table has been included in the pomp
object, then on a call to this function, covars
will be filled with the values, at time t
, of the covariates.
This is accomplished via interpolation of the user-supplied covariate table.
Additional arguments may be given: these will be filled by the correspondingly-named elements in the userdata
slot of the pomp
object (see pomp
).
If step.fun
is written in a native language, it must be a function of type
1 | pomp_onestep_sim
|
as specified in the header ‘pomp.h’ included with the package. Execute
1 | file.show(system.file("include/pomp.h",package="pomp.h"))
|
to view this file.
If rate.fun
is written as an R function, it must have at least the arguments j
, x
, t
, params
, and ...
.
Here, j
is the an integer that indicates for which of the elementary events the current rate is desired.
x
is a named vector containing the value of the state process at time t
, and
params
is a named vector containing parameters.
If the argument covars
is included and a covariate table has been included in the pomp
object, then on a call to this function, covars
will be filled with the values, at time t
, of the covariates.
This is accomplished via interpolation of the covariate table.
If rate.fun
is a native function, it must be of type
1 | pomp_ssa_rate_fn
|
as defined in the header ‘pomp.h’; see above for instructions on how to view this file.
In writing dens.fun
, you must assume that no state transitions have occurred between t1
and t2
.
If dens.fun
is written as an R function, it must have at least the arguments x1
, x2
, t1
, t2
, params
, and ...
.
On a call to this function, x1
and x2
will be named vectors of state variables at times t1
and t2
, respectively.
The named vector params
contains the parameters.
If the argument covars
is included and a covariate table has been included in the pomp
object, then on a call to this function, covars
will be filled with the values, at time t1
, of the covariates.
If the argument covars
is included and a covariate table has been included in the pomp
object, then on a call to this function, covars
will be filled with the values, at time t1
, of the covariates.
This is accomplished via interpolation of the covariate table.
As above, any additional arguments will be filled by the correspondingly-named elements in the userdata
slot of the pomp
object (see pomp
).
If dens.fun
is written in a native language, it must be a function of type
1 | pomp_onestep_pdf
|
as defined in the header ‘pomp.h’ included with the package; see above for instructions on how to view this file.
onestep.sim
, euler.sim
, discrete.time.sim
, and gillespie.sim
each return functions suitable for use as the argument rprocess
argument in pomp
.
onestep.dens
returns a function suitable for use as the argument dprocess
in pomp
.
Aaron A. King kingaa at umich dot edu
pomp
and the tutorials on the package website.
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