用lsoda、R语言定义结果阈值

我对R中deSolve包中的lsoda有一个问题（它可能也适用于ode函数）。我正在用一组ODE来模拟食物网的动态，计算两个相同食物网中5个物种的丰度，这两个食物网通过扩散连接在一起。 丰度以2000个时间步长计算，不应为负值或小于1e-6。在这种情况下，结果应更改为0。我找不到lsoda将负结果变为零的任何参数。我在ODE函数中尝试了以下技巧：

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你的全部代码是什么？我刚刚编辑了这个问题。添加了ODE函数和ODE公式的代码。谢谢你的全部代码是什么？我刚刚编辑了这个问题。添加了ODE函数和ODE公式的代码。谢谢

solve.model <- function(t,y, parms){

    y <- ifelse(y<1e-6, 0, y)
    #ODE functions here
    #...
    #...
    return(list(dy))
}

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)


        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

            #Calculating sigmas in denominator of Holing type II functional response
            (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
            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 #Change in the resource
        }

        #Calculating dispersals .they can be easily replaced
        # by adjacency maps of connections between food webs arbitrarily!

        # added to solve the problem of negative abundances
        deltas <- append(c(dy1), dy2)
        return(list(append(c(dy1),dy2)))    
    })
}

temp.abund[[j]] <- lsoda(y=initials, func=solve.model, times=0:max.time, parms=parms)