[R] OOP like handling of lists?

[Thomas Petzoldt]

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))