Tuesday, October 29, 2013

A first attempt at an individual-based model in R



I have been curious for a while as to how R might be used for the construction of an individually-based model (IBM), or agent-based model (ABM). In particular, what R objects lend themselves best to storing information on individuals, and allow for new individuals to be added or subtracted throughout the simulation?

In this first attempt, I have ended up opting for a multi-level list, where elements represent individuals, and sub-levels contain attribute information. The main reason is speed - In a previous post I highlighted the fact that lists are not penalized in the same way as a data.frame when the object in "grown" or concatenated with additional information (due to time spent reallocating memory).

The example models a population with a given birth rate, death rate, and carrying capacity. Attributes of individuals that are recorded in the list include their age, whether they are alive or dead, and their color (blue or red). The attribute of color is passed from parent to offspring, and there is a tendency for one phenotype to dominate over time.  The idea comes from a simple model of genetic drift that can be explored with the IBM programming platform NetLogo.

So far so good, but there are still some speed issues associated with a list that continues to grow. Part of the speed issue is due to the calculation of summary statistics during each iteration (using e.g. sapply). A cropping of the list to retain only alive individuals, has dramatic improvements on speed as well, so there appears to be a cost associated with growing the list object itself. The difference might be that my previous example was filling a empty list, where the number of elements was predefined. 

I would be interested if anyone has any thoughts on this or, more generally, on the construction of IBMs in R. While R is probably not the best programming language suitable to IBMs, it would be interesting to know if more examples exist.



Example:
b <- 0.14 # probability of birth
d <- 0.08 # probability of death
K <- 100 # carrying capacity
N0 <- 50 # starting number of individuals
t <- 500 # time of simulation
 
#create starting individual w attributes ("alive", "age", "color")
set.seed(1)
ind <- vector(mode="list", N0)
for(i in seq(ind)){
 ind[[i]]$alive <- 1
 ind[[i]]$age <- 0
 ind[[i]]$color <- c("blue", "red")[round(runif(1)+1)]
}
 
#make empty vectors to record population statistics
time <- seq(t+1)
 
pop <- NaN * time # population size
pop[1] <- N0
 
frac.blue <- NaN * time # fraction of population that is blue
cols <- sapply(ind, function(x) x$color)
frac.blue[1] <- sum(cols  == "blue") / length(cols)
 
med.age <- NaN * time
ages <- sapply(ind, function(x) x$age)
med.age[1] <- median(ages)
 
 
#simulation
save.alive.only <- TRUE # optional cropping of "ind" to include alive individuals only 
t1 <- Sys.time()
for(i in seq(t)){ # loop for each time increment
 
 is.alive <- which(sapply(ind, function(x) x$alive) == 1)
 for(j in is.alive){ #loop for each alive individual
  birth <- runif(1) <= (b * (1 - length(is.alive)/K)) # calculate a birth probability for each individual that is alive
  if(birth){
   len.ind <- length(ind)
   ind[[len.ind+1]] <- list(alive=1, age=0, color=ind[[j]]$color) # create offspring, inherits color of parent
  }
  death <- runif(1) <= d # calculate a death probability for each individual 
  if(death){
   ind[[j]]$alive <- 0 # if death, reset alive = 0
  } else { #else, advance age + 1
   ind[[j]]$age <- ind[[j]]$age + 1 # advance age of parent
  }
 }
 
 #optional cropping of list "ind"
 if(save.alive.only){
  is.dead <- which(sapply(ind, function(x) x$alive) == 0)
  if(length(is.dead) > 0) ind <- ind[-is.dead]
 }
 
 #Population stats
 is.alive <- which(sapply(ind, function(x) x$alive) == 1)
 pop[i+1] <- length(is.alive) 
 
 cols <- sapply(ind, function(x) x$color)
 frac.blue[i+1] <- sum(cols[is.alive]  == "blue") / length(is.alive)
 
 ages <- sapply(ind, function(x) x$age)
 med.age[i+1] <- median(ages[is.alive])
 
 print(paste(i, "of", t, "finished", "[", round(1/t*100), "%]"))
}
t2 <- Sys.time()
dt <- t2-t1
dt
 
 
#plot populations
png("pops_vs_time.png", width=6, height=4, units="in", res=400)
par(mar=c(4,4,1,1))
pop.blue <- pop * frac.blue
pop.red <- pop * (1-frac.blue)
ylim=range(c(pop.blue, pop.red))
plot(time, pop.blue, t="l", lwd=2, col=4, ylim=ylim, ylab="Population size")
lines(time, pop.red, lwd=2, col=2)
legend("topleft", legend=c("blue pop.", "red pop."), lwd=2, col=c(4,2), bty="n")
dev.off()
 
#plot median age
png("med_age_vs_time.png", width=6, height=4, units="in", res=400)
par(mar=c(4,4,1,1))
plot(time, med.age, t="l", lwd=2, ylab="Median age")
dev.off()
Created by Pretty R at inside-R.org


2 comments:

  1. I've been wondering about how to put together not just iBM's, but how to populate nicely-analyzable structures in general. What about putting individuals in an object-oriented framework, with each individual being represented by a single object? You would also have collections to access all individuals, etc.

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    Replies
    1. I think this is the strategy of the R package simecol, but I haven't had time to look into it. Maybe you're willing to write a guest post on the approach?

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