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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