Dear fellows from R-sig-Ecology,

Given that I am not familiar with general linear models, I would like to
consult you for a specific situation that I am facing with my dataset. I am
trying to calculate the predation risk of an heterospecific interaction
based on the size differences of the individuals and two other categorical
variables. My dataset is composed by the following variables:

Predation: binary (0 or 1)

Size difference: continuous (mm)

Treatment: categorical (group A, group B)

Pairing: categorical (species a-b, species b-a)

I am using the “mle2” function from the package “bbmle”, based on a binomial
distribution.

The models that I am testing are the following:

Model 1: null model:

> m1 <- mle2 (k ~ dbinom (prob = p, size = 1), start = list (p = .5), data =
list (N =1, k=predation))

Model 2: predation ~ size difference:

> modlogist = function (x, a, b) {

>             exp (a + b * (x))/(1 + exp (a + b * (x)))

>             }

> m2 <- mle2 (k ~ dbinom (prob = modlogist (size difference, a, b), size =
1),

+ start = list (a = 0, b =0), data = list (N = 1, k = predation))

Model 3: predation ~ size difference*treatment:

> start.3 = as.list (coef (m2))

> m3 <- mle2 (k ~ dbinom (prob = modlogist (size difference, a, b), size =
1), start = start.3,

+ data = list (N = 1, k = predation), parameters = list (a ~ treatment, b ~
treatment))



Model 4: predation ~ size difference*pairing

> start.4 = as.list (coef (m2))

> m4 <- mle2 (k ~ dbinom (prob = modlogist (size difference, a, b), size =
1), start = start.4,

+ data = list (N = 1, k = predation), parameters = list (a ~ pairing, b ~
pairing))



Model 5: predation ~ size difference*treatment:pairing

> start.5 = as.list (coef (m2))

> m5 <- mle2 (k ~ dbinom (prob = modlogist (size difference, a, b), size =
1), start = start.5,

                + data = list (N = 1, k = predation), parameters = list (a ~
treat:pair, b ~ treat:pair))



For one of the pairing groups (let say species a-b) not a single predation
event occurred. When I call the summary of the 4th model, the standard
errors of the estimates are absurdly high. Initially I thought that this
could be related to the optimization algorithm. However, even performing
some of the basic changes in the “mle2” optimization parameters I could not
find a proper solution.

Can anyone suggest me where to find the necessary information in order to
solve this situation? Are there any other points that I am missing here?

Thanks in advance!

Ernesto

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