Hi,
I don't yet completely understand what you want, but maybe the following
can help you a little bit.
1) your code is not completely correct, as parms$c does not contain the
interaction matrix. Add a print to your function to see this:
c <- params$c
print(c)
2) using for-loops in a model function is quite unusual and, together with
calculating sums a strong indication to me, that you should consider matrix
multiplication, see page 124 vs. 123 in our tutorial:
http://desolve.r-forge.r-project.org/user2014/tutorial.pdf#124
That example shows a multi-species Lotka-Volterra model. Could it be
possible that you want something like that?
3) I wonder also, why you use c*P[i] and not c[i,j] and why all
interactions are positive.
4) Finally: in ODE models, "time steps" are infinitesimally small (i.e.
close to zero). This means that interaction happens instantaneously or with
other words: all variables are a result from previous time. Time is
continuous, so time "step" and "step before" do not make much sense --
except the technical fact that the solver approximates continuous time with
finite steps; or "time delay" for delay differential equations, but this
would be another story.
Regards,
Thomas P.
Am 27.02.2017 um 05:02 schrieb Guadalupe Peralta:
funcPA <- function (t, y, params, nsp) {
with(as.list(c(y,params)), {
nP <- nsp[1]
nA <- nsp[2]
ntot <- nP + nA
P <- y[1:nP]
A <- y[(nP + 1):ntot]
dP <- array(0, nP)
dA <- array(0, nA)
c <- params$c
for (i in 1:nP) {
for (j in 1:nA){
dP[i] <- P[i] + sum(c*A[j]*P[i])
dP
dA[j] <- A[j] + sum(c*P[i]*A[j])
dA }}
res <-list(c(dP, dA))
})
}
nsp <- c(nP=3, nA=2)
c <- list()
c[[1]] <- matrix(c(0.1,0.11,0.12,0,0,0.1),nrow=3, ncol=2)
y <- c(P1=2, P2=1, P3=2, A1=1, A2=2)
ode(y=y, times=seq(0,1,by=0.1), func=funcPA, nsp=nsp, parms=c)