| iteration {simecol} | R Documentation |
Solver function to simulate discrete ecological (or other) dynamic
models. It is normally called indirectly from sim.
iteration(y, times=FALSE, func=FALSE, parms=FALSE, animate = FALSE, ...)
y |
the initial values for the system. If y has a
name attribute, the names will be used to label the output matrix. |
times |
times at which explicit estimates for y are
desired. The first value in times must be the initial time. |
func |
a user-supplied function that computes the values of the
next time step (not the derivatives !!!)
in the system (the model defininition) at time t.
The user-supplied function func must be called as:
yprime = func(t, y, parms). t is the current time point
in the integration, y is the current estimate of the variables
in the ode system, and parms is a vector of parameters (which
may have a names attribute, desirable in a large system).
The return value of func should be a list, whose first element is a vector containing the derivatives of y with respect to
time, and whose second element is a vector (possibly with a
names attribute) of global values that are required at
each point in times. |
parms |
vector or list holding the parameters used in func
that should be modifiable without rewriting the function. |
animate |
Animation during the simulation (if available for the specified class. |
... |
optional arguments passed to the plot function if
animate=TRUE. |
The solver method iteration is used to simulate discrete event
models. Normally, this function is run indirectly from
sim.
In contrast to differential equation solvers, the main function
of the model must not return the first derivative but instead and
explicitly the new state at the specified times.
The actual value of time is available in the main function as
time and the current increment as parms["DELTAT"] or
parms$DELTAT. It is element of a vector if parms is a
vector and it is a list if parms is a list.
If iteration is used for difference equations (see example
dlogist below), it is mandatory to multiply the incremental
part with DELTAT to ensure that variable time steps are
correctly respected and that the first row of the simulation outputs
contains the states at t_0.
The default iteration method of class simObj supports
the observer mechanism. This means that a function stored in
slot observer is called during each iteration step with the
return value of main as its first argument. You can use this to
control the amount of data stored during each iteration step
(e.g. whole population or only mean values for individual based
models), to do run-time animation or to write log files.
A list of the model outputs (states ...) for each timestep.
sim, parms,
lsoda, rk4, euler.
data(conway)
## plot after simulation:
plot(sim(conway), delay=100)
## plot during simulation
sim(conway, animate=TRUE, delay=100)
## discrete version of logistic growth equation
## Note: function main returns the *new value*, not the derivative
dlogist <- new("odeModel",
main = function (time, init, parms, ...) {
x <- init
with(as.list(parms), {
x <- x + r * x * (1 - x / K) * DELTAT
# ^^^ add to old value ^^^^^^ special parameter with time step
list(c(x))
})
},
parms = c(r=0.1, K=10),
times = seq(0, 100, 1),
init = c(population=0.1),
solver = "iteration" #!!!
)
plot(sim(dlogist))
## alternative with function that returns the derivative
## discrete steps are realized with the euler method
dlogist <- new("odeModel",
main = function (time, init, parms, ...) {
x <- init
with(as.list(parms), {
x <- r * x * (1 - x / K)
list(c(x))
})
},
parms = c(r=0.1, K=10),
times = seq(0, 100, 1),
init = c(population=0.1),
solver = "euler"
)
plot(sim(dlogist))