| 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.
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"]
depending on the data type of parms.
Normally, this function is run indirectly from sim.
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 determine 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
dlogist <- new("odeModel",
main = function (time, init, parms, ...) {
x <- init
p <- parms
x[1] <- x[1] + p["r"] * x[1] * (1 - x[1] / p["K"])
# ^^^^ important !!! new value, not derivative
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))
parms(dlogist)["r"] <- 2
plot(sim(dlogist))