simecol-package {simecol} | R Documentation

## General Information about the ‘simecol’ Package

### Description

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. For this purpose, an object oriented approach using the S4 class system is proposed, which should provide a consistent but still flexible and extensible way to implement simulation models of different types, namely:

- ordinary differential equation (ODE) 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 `fixParms`.
- `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). It is up to the user to evaluate the corresponding values within the `main` function. 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 addition, solvers from package `"ddesolve"` can also be used. 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.

  Note that the `observer`-mechanism works only with `iteration` solvers. It is generally 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 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`).

In this way, a `simObj` object may contain all data, which are needed to run simulations simply by entering the model object via `source()` or `data()` and then to run and plot the model for example via `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 or initfunc or 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 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 are intended as a starting point to write your 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.

### Author(s)

Thomas Petzoldt thomas.petzoldt@tu-dresden.de

### References

Petzoldt, T and K. Rinke (2007) simecol: An Object-Oriented Framework for Ecological Modeling in R. Journal of Statistical Software, 22(9). URL http://www.jstatsoft.org/v22/i09/.

### See Also

`CA`, `chemostat`, `conway`, `diffusion`, `lv`, `lv3`, `upca`.

### Examples

    ## (1) Quick Start Examples ====================================================

    data(lv)        # load basic Lotka-Volterra model

    ## Not run:
    fixParms(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
    fixInit(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))

    ## (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"), "/examples", sep=""))

    ## show package vignette with introductory article
    vignette("simecol-introduction")
    edit(vignette("simecol-introduction"))

    ## open the installation directory of simecol
    ##   (NEWS, THANKS, FAQ, ToDo, WISHLIST, ...)
    browseURL(paste(system.file(package="simecol"), "/", sep=""))

    ## Open Project Homepage
    browseURL("http://www.simecol.de")
    ## End(Not run)

    ## How to cite package simecol in publications
    citation("simecol")

[Package simecol version 0.6-9 Index]