Hello,

I am writing a package with a collection of several models. In order to allow users to play interactively with the models (in contrast to hacking lengthy scripts), I want to put all what is needed to run a particular model into a single list object for each model.

Then there will be a collection of functions to run the model or to modify parameters, time steps, integration method ..., which should *work on the list itself* or make a copy of it.

An example:

the model object may have the name "lvmodel" (see below). so it can be simulated and plotted simply using:

lvmodel <- simulate(lvmodel)
plot(lvmodel)

Parameters (and other stuff) may be modified with:

getParams(lvmodel)
lvmodel <- setParms(lvmodel, list(k1=0.5))

... and then simulated and plotted again.

The problem however is, that for functions which modify list elements an assignement to a new (or the same) variable MUST exist.

I want to write simply:

setParms(lvmodel, list(k1=0.5))

and not

lvmodel <- setParms(lvmodel, list(k1=0.5))

or at least get a warning, if the assignement is missing. I don't want to break the R philosophy of function parameter handling. On the other side the full OOP-approach in R-News 1(2002)3 of Chambers and Lang works, but may be a little bit to complicated to explain it to my collegues and students. So, is there an alternative to do such things, which I may have overlooked?

Thank you!

Thomas Petzoldt


##################################################################### #A simplified working example #####################################################################

library(odesolve)

## The differential equation model ##################################
lvmodel<-list(
    equations = function(t, x, p) {
      dx1.dt <-   p["k1"] * x[1] - p["k2"] * x[1] * x[2]
      dx2.dt <- - p["k3"] * x[2] + p["k2"] * x[1] * x[2]
      list(c(dx1.dt, dx2.dt))
    },
    parms  = c(k1=0.2, k2=0.2, k3=0.2),
    xstart = c(prey=0.5, predator=1)
    # and some more elements ...
)
class(lvmodel) <- "odemodel"

## Getting and setting parameters ###################################

getParms <- function(model) {
  model$parms
}

setParms <- function(model, parmlist) {
  for (i in 1:length(parmlist)) {
    model$parms[names(parmlist[i])] <- parmlist[[i]]
  }
  invisible(model)
}

## Simulation #######################################################

simulate <- function(model, ...) {
  times <- seq(0, 100, 0.1)
  res <- lsoda(model$xstart, times, model$equation, model$parms, ...)
  model$out <- as.data.frame(res)
  model
}

## Plotting

plot.odemodel <- function(model) {
    oldpar <- par(no.readonly=TRUE)
    par(mfrow=c(2, 1))
    nam <- names(model$out)
    for (i in 2:ncol(model$out)) {
      plot(model$out[[1]], model$out[[i]],
           type="l", xlab=nam[1], ylab=nam[i])
    }
    par(oldpar)
}

#### MAIN PROGRAM #########

lvmodel <- simulate(lvmodel)
plot(lvmodel)

getParms(lvmodel)
lvmodel <- setParms(lvmodel, list(k1=0.5))
plot(simulate(lvmodel))

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