Altering design matrix for 'real' covariate

Forum for discussion of general questions related to study design and/or analysis of existing data - software neutral.

Altering design matrix for 'real' covariate

Postby pat_14 » Fri Sep 24, 2021 1:04 pm

Hi all,

I'm attempting to add a linear constraint to my recovery probability estimates in a dead recovery (Brownie) model using MARK GUI. I have 17 sampling intervals, and want survival to be time-invariant, but recovery probabilities to be constrained by a 'real' covariate I've supplied.

I've reconstructed the design matrix, but do not think I have done it correctly. I've had trouble uploading a picture of my design matrix so I'll try and describe it:

The design matrix has three columns (S_intercept, f_intercept, f_cov) and of course the Parm. For my S_intercept column, I've placed 1's for the first 16 rows pertaining to my survival estimates, and zeros for the rest of the rows. My f_intercept column contains the opposite configuration (zeros for the first 16 rows, and 1s for the next 17 rows). The f_cov column contains zeros for the first 16 rows pertaining to the survival estimates, and the 'real' data for the next 17 rows pertaining to the recovery probabilities.

Does this sound even remotely correct? Any suggestions would be greatly appreciated!
pat_14
 
Posts: 6
Joined: Wed Jun 16, 2021 2:45 pm

Re: Altering design matrix for 'real' covariate

Postby cooch » Fri Sep 24, 2021 1:22 pm

Sounds correct. At some point, you're likely going to want to generate a plot of 'recovery probability' versus the environmental covariate. This can be done in a fashion within MARK -- averaging over models. In fact, you do this by 'tricking' MARK into treating the environmental covariates as individual covariates (section 11.8.2 in Chapter 11).
cooch
 
Posts: 1546
Joined: Thu May 15, 2003 4:11 pm
Location: Cornell University

Re: Altering design matrix for 'real' covariate

Postby pat_14 » Fri Sep 24, 2021 2:58 pm

Thanks! Surprisingly, the model with the added covariate had lower weight than the other four pre-defined models. I suppose that tells me I need to find a better covariate!
pat_14
 
Posts: 6
Joined: Wed Jun 16, 2021 2:45 pm

Re: Altering design matrix for 'real' covariate

Postby cooch » Fri Sep 24, 2021 6:08 pm

pat_14 wrote:Thanks! Surprisingly, the model with the added covariate had lower weight than the other four pre-defined models. I suppose that tells me I need to find a better covariate!


Uh, no. You (the biologist) should think hard about the set of covariates that you (said biologist) think are relevant, or interesting, or important. You construct a prior a candidate model set - including models with and without the covariate(s) - and use some modality to evaluate support over the models for (or not) the covariates. You then draw your conclusions. Model building should not be an exercise in hunting around for a covariate that 'does better' (say, by reducing AIC relative to other models). Queue concerns about 'data dredging'. If you look through a large enough set of covariates, you will by random chance along likely find one that seems to do better -- but that could be entirely spurious, and beyond simple interpretation.
cooch
 
Posts: 1546
Joined: Thu May 15, 2003 4:11 pm
Location: Cornell University


Return to analysis & design questions

Who is online

Users browsing this forum: No registered users and 1 guest