Hello,
I'm using MARK for the first time and I got confused about the parameter counting as described in the addenum of Chapter 4 and Appendix F of the book. As far as I understood I need to correct the number of estimatable parameters in order to get the right weightings for the models.
I'm doing a Burnham Joint live encounter and dead recovery analysis, which has the parameters S (survival), p (properbility live resight), r (properbility of dead report), and F (dispersal from the sample).
First I want to fit a basic model by trying all combinations of constant, time, and/or group dependent parameters. Then I will check for age dependence of this model in a similar fashion.
For both these steps I don't specify a design matrix, so I can use the sin link funktion. According to chapter 4 this is quiet good for getting the right parameter count.
As I understood, as soon as one of the parameters is constant, all parameters are estimatable. For the other cases I should use the procedure from chapter 4, and count unique parameters in the table of saturated capture histories.
In the second step i want to include individual covariates. Since this includes using a design matrix, the logit link funktion has to be used. MARK seems to make far more errors with this one.
For a basic Model with at least one constant parameter, can I use the number of columns in the DM as the number of estimatable parameters?
So can I use this procedure?:
no individual covariates -> At least one constant parameter: number of parameters= estimatable parameters
No constant parameter : use table of saturated encounter histories
individual covariates -> Number of collumns in DM= estimatable parameters
Thank You!