using events in deSolve to prevent negative state variables, R - r

Question

I am modeling the population change in a food web of species, using ODE and deSolve in R. obviously the populations should not be less than zero. therefore I have added an event function and run it as below. although the answers change from when I did nlt used event function, but it still producds negative values. What is wrong?
#using events in a function to distinguish and address the negative abundances
eventfun <- function(t, y, parms){
y[which(y<0)] <- 0
return(y)
}
# =============================== main code
max.time = 100
start.time = 50
initials <- c(N, R)
#parms <- list(webs=webs, a=a, b=b, h=h, m=m, basals=basals, mu=mu, Y=Y, K=K, no.species=no.species, flow=flow,S=S, neighs=neighs$neighs.per, dispers.maps=dispers.maps)
temp.abund <- ode(y=initials, func=solve.model, times=0:max.time, parms=parms, events = list(func = eventfun, time = 0:max.time))
and here is the ODE function(if it helps in finding the problem):
solve.model <- function(t, y, parms){
y <- ifelse(y<1e-6, 0, y)
with(parms,{
# return from vector form into matrix form for calculations
(R <- as.matrix(y[(max(no.species)*length(no.species)+1):length(y)]))
(N <- matrix(y[1:(max(no.species)*length(no.species))], ncol=length(no.species)))
dy1 <- matrix(nrow=max(no.species), ncol=length(no.species))
dy2 <- matrix(nrow=length(no.species), ncol=1)
no.webs <- length(no.species)
for (i in 1:no.webs){
species <- no.species[i]
(abundance <- N[1:species,i])
adj <- as.matrix(webs[[i]])
a.temp <- a[1:species, 1:species]*adj
b.temp <- b[1:species, 1:species]*adj
h.temp <- h[1:species, 1:species]*adj
(sum.over.preys <- abundance%*%(a.temp*h.temp))
(sum.over.predators <- (a.temp*h.temp)%*%abundance)
#Calculating growth of basal
(basal.growth <- basals[,i]*N[,i]*(mu*R[i]/(K+R[i])-m))
# Calculating growth for non-basal species D
no.basal <- rep(1,len=species)-basals[1:species]
predator.growth<- rep(0, max(no.species))
(predator.growth[1:species] <- ((abundance%*%(a.temp*b.temp))/(1+sum.over.preys)-m*no.basal)*abundance)
predation <- rep(0, max(no.species))
(predation[1:species] <- (((a.temp*b.temp)%*%abundance)/t(1+sum.over.preys))*abundance)
(pop <- basal.growth + predator.growth - predation)
dy1[,i] <- pop
dy2[i] <- 0.0005 #to consider a nearly constant value for the resource
}
#Calculating dispersals .they can be easily replaced
# by adjacency maps of connections between food webs arbitrarily!
disp.left <- dy1*d*dispers.maps$left.immig
disp.left <- disp.left[,neighs[,2]]
disp.right <- dy1*d*dispers.maps$right.immig
disp.right <- disp.right[,neighs[,3]]
emig <- dy1*d*dispers.maps$emigration
mortality <- m*dy1
dy1 <- dy1+disp.left+disp.right-emig
return(list(c(dy1, dy2)))
})
}
thank you so much for your help

Answer 1

I have had success using a similar event function defined like this:
eventfun <- function(t, y, parms){
with(as.list(y), {
y[y < 1e-6] <- 0
return(y)
})
}

Answer 2

I am using a similar event function to the one posted by jjborrelli. I wanted to note that for me it is still showing the ode function returning negative values. However, when ode goes to calculate the next step, it is using 0, and not the negative value shown for the current step, so you can basically ignore the negative values and replace with zeros at the end of the simulation.