Joachim Audenaert <Joachim.Audenaert <at> pcsierteelt.be> writes:
> When I try to estimate the functional response of the Rogers type I
> equation (for the mle2 you need the package bbmle):
>
> > RogersIbinom <- function(N0,attackR2_B,u_B) {attackR2_B+u_B*N0}
> > RogersI_B <-
> mle2(FR~dbinom(size=N0,prob=RogersIbinom(N0,attackR2_B,u_B)/N0),
start=list(attackR2_B=4.5,u_B=0.16),method="Nelder-Mead",data=data5)
>
> I get following error message
>
> Error in optim(par = c(4.5, 0.16), fn = function (p) :
> function cannot be evaluated at initial parameters
>
> Can someone tell me what I'm doing wrong? I used estimate starting values
> which were predicted with the nls function
>
> RogersI_N <-
> nls(FR~attackR2_N+u_N*N0,start=list(attackR2_N=1,u_N=4),
> control=list(maxiter=10000))
It's hard to know without a reproducible example.
I'm a little confused by the broader context here because it looks
like your "Rogers functional response" is just linear?? (In the context
of functional response models in ecology I think of the Rogers random
predator equation -- maybe I have the wrong idea.)
If your predicted per capita attack probability is greater than
1 for any case, then you're going to be in trouble here ...
Ben Bolker
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