simecol-package: Simulation of Ecological (and Other) Dynamic Systems
In simecol: Simulation of Ecological (and Other) Dynamic Systems

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, individual-based (or agent-based) and other models as well. It supports structuring of simulation scenarios (to avoid copy and paste) and aims to improve readability and re-usability of code.

The DESCRIPTION file: This package was not yet installed at build time.

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),
grid-oriented individual-based models (class gridModel), and
particle diffusion-type models (class rwalkModel),
individual-based 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 non-nested list holding arbitrary sub-equations (sub-models) of the model. Sub-equations 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 from-to-by form can be edited with editParms.
init: initial state (start values) of the simulation. This is typically a named vector (state variables in odeModels) or matrix (e.g. initial grid of gridModels).
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 individual-based 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 user-provided observer function can also be used to write logging information to the screen or to the hard-disk, to perform run-time visualisation, or statistical analysis during the simulation.

The observer-mechanism works only with iteration solvers. It is not available for odeModels.
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 user-definded 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 re-initialized 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 sub-equations, 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 lv@main, 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 [aut, cre] (<https://orcid.org/0000-0002-4951-6468>)

Petzoldt, T. and K. Rinke (2007) simecol: An Object-Oriented 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) Quick Start Examples ====================================================

data(lv)        # load basic Lotka-Volterra 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 multi-species 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))