I am working on the movement of fish species from the centre of a protected area to a non-protected area. Based on the article by R. Abesamis, itself inspired by the work of B. Kaunda-Arar (page 91), I applied to my dataset a logistic decay D = 1/(1+exp(S*(I-d)) where D is the proportion of biomass in the middle of the reserve, d is the distance from the centre of the protected area, S is the slope and I is the inflection point. The parameters to be estimated in this model are S and I. However, I had to use Excel to estimate these parameters because the
nls function in R was giving me constant error messages (the well-known singular gradient error). I successfully estimated S and I using the Excel solver, but I need to know the standard errors of these parameters. For this, the article states that a linearization of my logistic model is necessary, but despite my research on this subject I do not understand how to do this.
My questions are therefore the following:
- Is it possible to get the standard error of my parameters directly from Excel? If so, how?
- If this is not possible in Excel, how can I get the standard errors of my S and I parameters in R? (note: using the
nlsfunction, even when applying the values obtained by my Excel model as starting values for S and I, the gradient error message is still displayed. Same thing when using