| simplePdmp | R Documentation | 
This is a simple example for a piecewise deterministic markov process
defined as pdmpModel. It is included to the package for 
demonstration purposes and is used in some unit tests and function 
examples.
simplePdmp
An object of class pdmpModel.
parmsThere are no parameters for this model.
initThere is one continous variable f with initial value 0
and one discrete variable d initial value 0.
discStatesThe discrete variable d has codomain {-1, 0, 1}.
dynfuncThe continous variable f evolves as linear function
f(t) = t if d = 1,
as f(t) = -t if d = -1 and is constant zero if d = 0. 
Its dynamic can therefore be described as \frac{df}{dt} = d.
jumpfuncThere are two jumptypes. The first jumps from d to 
d - 1, the second from d to d + 1. Both reset f 
to zero.
ratefuncA vector of length two determining the probability of a jump 
being of type 1 or 2. If d = -1, it has value (0, 2) forcing 
the jumptype to be of type 2. The same takes place for d = 1: 
ratefunc returnes (2, 0) and the jumptype is therefore always 
of type 1. In case d = 0, ratefunc returnes (1, 1) which
leads to a probability of \frac{1}{2} to have a jumptype of type 
1 or 2.
timesThe simulations will start at time t = 0 and end at 
t = 10 with step length 0.01.
toggleSwitch for a more sophisticated example of a 
pdmpModel and pdmpModel
for the formal description of the S4 class.
## the code used to generate this model:
simplePdmp <- pdmpModel(
    descr = "A simple PDMP",
    init = c(f = 0, d = 0),
    discStates = list(d = c(-1, 0, 1)),
    times = c(from = 0, to = 10, by = 0.01),
    dynfunc = function(t, x, parms) c(x["d"], 0),
    ratefunc = function(t, x, parms) c(1+x["d"], 1-x["d"]),
    jumpfunc = function(t, x, parms, jtype){
         c(0, switch(jtype, x["d"]-1, x["d"]+1))
    })
## load it and plot a simulation:
data("simplePdmp")
plot(sim(simplePdmp))
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