by darryl » Thu Jan 20, 2011 4:58 pm
1) do you have an estimated value of p=1 for each and every survey? With rainfall as a covariate then PRESENCE should be giving you more than 1 value. Like Dave says, if p really =1 that should be obvious from your raw data.
2) Unlike MARK, PRESENCE splits the design matrix up by parameter type. Otherwise, how you use the design matrices is very MARK-like. The design matrices you've included say that you don't have any covariates that allow first year occupancy, colonization or extinction to vary amongst sampling units (ie sites). For detection you don't have an intercept term in there which creates a very restrictive relationship that will force the regression line to go through the origin of an plot, ie you're forcing logit(p) = 0 when rainfall = 0, with logit(p) = 0 evaluating to p = 0.5. Hence, by not including the intercept you're assuming that when there's no rainfall, your detection probability is exactly 0.5. Generally you're going to want to have a column of 1's in the design matrix, or at least one 1 somewhere along each row depending how you want to interpret some of the estimates.
3) Jim Hines is starting to add some model averaging capability into PRESENCE, and it appears in the menu of the latest version, but it's still a work in progress. By hand is the main option at the moment.
4) See Bret's reply for where. You don't have an intercept term in your model for detection so you won't be able to find one. Don't understand the second part of your question, by definition intercepts relate to covariate values of 0. The real parameters that get reported use the covariates values that are in the data file.
5) Personally, with no claims its the right thing to do, if I'm thinking about using a 2-step approach then rather than having the other parameter types as simple constants, I'd make them fairly general by including the covariates of interest for those parameters. My logic is that this gives the model some flexibility in case there really is variation amongst units that wouldn't be getting represented if you just assumed the parameters were constant. If you're inappropriately assuming the other parameters are constant, then you may get misleading results for which covariates are important for detection.