Description Details Author(s) References See Also Examples
An object oriented framework to simulate ecological (and other) dynamic systems. It can be used for differential equations, individualbased (or agentbased) and other models as well. It supports structuring of simulation scenarios (to avoid copy and paste) and aims to improve readability and reusability of code.
The DESCRIPTION file:
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The simecol package is intended to give users (scientists and students) an interactive environment to implement, distribute, simulate and document ecological and other dynamic models without the need to write long simulation programs. An object oriented framework using the S4 class system provides a consistent but still flexible approach to implement simulation models of different types:
differential equation (ODE, PDE) models (class odeModel
),
gridoriented individualbased models (class gridModel
), and
particle diffusiontype models (class rwalkModel
),
individualbased models (class indbasedModel
),
other model types by deriving a user specified subclass from
simObj
.
Each simulation model is implemented as S4 object (superclass simObj
)
with the following slots:
main = function(time, init, parms, ...)
: a function
holding the main equations of the model,
equations
: an optional nonnested list holding
arbitrary subequations (submodels) of the model. Subequations
can be interdependent and can be called directly from within
main
or initfunc
.
parms
: a list (or vector for some classes) with
constant model parameters,
times
: vector of time steps or vector with three
named values from
, to
, by
specifying the
simulation time steps. The fromtoby form can be edited with
editParms
.
init
: initial state (start values) of the
simulation. This is typically a named vector (state variables in
odeModel
s) or matrix (e.g. initial grid of
gridModel
s).
inputs
: time dependend or spatially resolved external
inputs can be specified as data frame or matrix (more
efficient). Performance optimized versions of approx
(see
approxTime
) are available.
solver
: a function or a character string specifying the
numerical algorithm used, e.g. "lsoda"
, "rk4"
or
"euler"
from package deSolve
). In contrast to
"euler"
that can be used for difference equations
(i.e. main
returns derivatives), "iterator"
is
intended for models where main returns the new state (i.e for
individualbased models). It is also possible to reference own
algorithms (solvers) that are defined in the user workspace or to
assign solver functions directly.
observer
: optional slot which determines the data
stored during the simulation. A userprovided observer
function can also be used to write logging information to the
screen or to the harddisk, to perform runtime visualisation, or
statistical analysis during the simulation.
The observer
mechanism works only with
iteration
solvers. It is not available for
odeModel
s.
out
: this slot holds the simulation results after a
simulation run as data frame (if the return value of main
is a vector) or as list (otherwise). The type of data stored in
out
can be manipulated by providing a userdefinded
observer
function.
initfunc
: this slot can hold an optional function which
is called automatically when a new object is created by new
or when it is reinitialized by initialize
or sim
.
simObj
model objects should be defined and created using the
common S4 mechanisms (new
).
Normally, a simObj
object can contain all data needed to run
simulations simply by entering the model object via source()
or
data()
and then to run and plot the model with
plot(sim(obj))
.
Accessor functions (with names identical to the slot names) are
provided to get or set model parameters, time steps, initial values,
inputs, the solver, the main and subequations, an observer or an
initfunc and to extract the model outputs. It is also possible to
modify the components of the simecol objects directly, e.g. the model
equations of a model lv
with [email protected]
, but this is
normally not recommended as there is no guarantee that this will work in a
compatible way in future versions.
Models of different type are provided as data and some more in source code (see directory examples).
The examples can be used as a starting point to write own
simObj
objects and to distribute them to whomever you wish.
The package is supplemented with several utility functions
(e.g. seedfill
or neighbours
), which can
be used independently from simObj
objects.
Thomas Petzoldt
Petzoldt, T. and K. Rinke (2007) simecol: An ObjectOriented Framework for Ecological Modeling in R. Journal of Statistical Software, 22(9). doi: 10.18637/jss.v022.i09
CA
,
chemostat
,
conway
,
diffusion
,
lv
,
lv3
,
upca
.
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 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95  ## (1) Quick Start Examples ====================================================
data(lv) # load basic LotkaVolterra model
## Not run:
require("tcltk")
lv < editParms(lv)
## End(Not run)
parms(lv)
main(lv)
lv < sim(lv)
plot(lv)
results < out(lv)
## Not run:
data(conway) # Conway's game of life
init(conway) < matrix(0, 10, 10)
times(conway) < 1:100
conway < editInit(conway) # enter some "1"
sim(conway, animate=TRUE, delay=100)
## End(Not run)
## (2) Define and run your own simecol model ==========================
lv < new("odeModel",
main = function (time, init, parms) {
with(as.list(c(init, parms)), {
dn1 < k1 * N1  k2 * N1 * N2
dn2 <  k3 * N2 + k2 * N1 * N2
list(c(dn1, dn2))
})
},
parms = c(k1 = 0.2, k2 = 0.2, k3 = 0.2),
times = c(from = 0, to = 100, by = 0.5),
init = c(N1 = 0.5, N2 = 1),
solver = "lsoda"
)
lv < sim(lv)
plot(lv)
## (3) The same in matrix notation; this allows generalization ====
## to multispecies interaction models with > 2 species. ====
LVPP < new("odeModel",
main = function(t, n, parms) {
with(parms, {
dn < r * n + n * (A %*% n)
list(c(dn))
})
},
parms = list(
# growth/death rates
r = c(k1 = 0.2, k3 = 0.2),
# interaction matrix
A = matrix(c(0.0, 0.2,
0.2, 0.0),
nrow = 2, ncol = 2, byrow=TRUE)
),
times = c(from = 0, to = 100, by = 0.5),
init = c(N1 = 0.5, N2 = 1),
solver = "lsoda"
)
plot(sim(LVPP))
## (4) Additional resources ============================================
## Not run:
## open the directory with source code of demo
browseURL(paste(system.file(package="simecol"), "/demo", sep=""))
## run demo
demo(jss)
## open the directory with R sourcecode examples
browseURL(paste(system.file(package="simecol"), "/doc/examples", sep=""))
## show package vignette with introductory article
vignette("asimecolintroduction")
vignette("bsimecolhowtos")
## edit a vignette
# edit(vignette("asimecolintroduction"))
# edit(vignette("bsimecolhowtos"))
## Open Project Homepage
browseURL("http://www.simecol.de")
## End(Not run)
## How to cite package simecol in publications
citation("simecol")

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