Simple loop coding issue

I am a new user of R and I have tried to write a script for similuting species invasion and community stability. I have almost finished it and I have only one tiny problem in a loop.

I have a pool of 40 species (1,2,...) and I create a community by successive invasions. Species in the community leave the invaders pool unless they go extinct(i put a density threshold value).

I want a lot of invasions (>4000) so I created a vector with 4000 number between 1 and 40 (random.order) but I have a problem because my matrix with the species density (init.x) has not the same number of elements as my vector.

time<- list(start=0,end=4000,steps=100)
# Initial conditions (set all species to zero in the beginning)
init.x <- runif(n)*0
# generate random order in which species are introduced
init.order<- sample(1:n)
order<-rep(order,100)
random.order<-sample(order,size=length(order))
outt <- init.x
**for (i in 1:4000){
    # Introduce 1 new species (according to vector "random.order") with freq 1000*tol
                # if the species is not yet in the init.x matrix  
    if (init.x[random.order[i]]<tol) {init.x[random.order[i]] <- 1000*tol}**
                # integrate lvm model
    out <-n.integrate(time=time,init.x=init.x,model=lvm)
    # save out and attach it to outt
                  outt <- rbind(outt,out)
    # generate new time window to continue integration
                  time <- list(start=ti开发者_开发问答me$end, end = time$end+time$end-time$start,
                     steps=100)
}       

I know this is probably very simple but I can't find out a way to write my loop to have more invasions than the number of species (number of raws in my matrix).

Thanks a lot,

---

You probably want to change

# Initial conditions (set all species to zero in the beginning)
init.x <- runif(n)*0
# generate random order in which species are introduced
init.order<- sample(1:n)
order<-rep(order,100)
random.order<-sample(order,size=length(order))

Into

# Initial conditions (set all species to zero in the beginning)
init.x <- rep.int(0, n) #should be a lot faster
# generate random order in which species are introduced
random.order<-sample.int(n,size=4000, replace=TRUE)

...to solve your main problem (check ?sample). I have not checked the rest of you code, but there may be room for more optimization.

---

I'm not clear on what your problem is, and what it going into outt. You may want to initalise it with list().

As for choosing a random invader you could try:

init.x[sample(which(init.x<tol),1)] <- 1000*tol

This avoids the if statement and the need for the pre-computed random trials (which may fail to produce an invasion if a community species is selected).

---

time<- list(start=0,end=1000,steps=1000)
# Initial conditions (set all species to zero in the beginning)
init.x <- runif(n)*0
# generate random order in which species are introduced
order <- sample(1:n)
outt <- init.x
for (i in 1:n){
    # Introduce 1 new species (according to vector "order") with freq 1000*tol
    init.x[order[i]] <- 1000*tol
    # integrate lvm model
    out <-n.integrate(time=time,init.x=init.x,model=lvm)
    # save out and attach it to outt
        outt <- rbind(outt,out)
    # generate new time window to continue integration
        time <- list(start=time$end, end = time$end+time$end-time$start,
                     steps=1000)
}