design matrix problem

[abreton]

"The DM represents a set of linear constraints applied to the underlying PIMs. If the PIMs change, then by definition so does the corresponding DM. "

Evan, I assumed this was obvious to everyone or that they had recently become aware of this 'phenomenon' through trial and error.

[cooch]

"The DM represents a set of linear constraints applied to the underlying PIMs. If the PIMs change, then by definition so does the corresponding DM. "

Evan, I assumed this was obvious to everyone or that they had recently become aware of this 'phenomenon' through trial and error.

No, it isn't obvious. If it were, I suspect this thread wouldn't have been started in the first place...

[cooch]

Hi All,
Yes, I did mean Brett. I've just seen Jeff's name on the forum so often...

If everyone can just humor me for a moment...
I understand that linear models have to be built in the DM, this is clear. The problem that I was trying to solve is how to pool multiple release groups from the same treatment and then use that for the global/starting model.
So for example, in response to Evan's 1st reply about pooling the male and female dippers, if we delete the column representing sex (and the interactions) in the DM then we are left with 6 columns and 12 rows for phi. Since we want the survival parameters for the entire group, we *know*, and have to remember, that the bottom half of the phi quadrant is to be ignored (rows 7-12). When we run the model we get 6 beta parameters for survival and 12 real/reconstituted parameters for survival. The real estimates for 1-6 and 7-12 are identical. Now we have to remember that parameters 7-12 in the real parameter output are to be ignored. With the dipper data set this is easy to figure out, but when we have huge DM's and multiple groups this could get very confusing.
Alternatively, we could pool the sexes in the PIM chart, open the corresponding DM, and wall-la!, there 6 rows for the phi parameters. There are also only 6 real phi parameters reported. (Could some one please try it and confirm this?) At this point you can add dummy or real covariates, add as many interactions as you please, to your pooled data with the DM.
I will venture to say that it sounds like this is what happens in RMark as well.
Erin

So, if all you wanted to do is pool right from the beginning, you could just as easily have done this right in the .INP file. Not only would that achieve the 'pooling' you want, but would reduce the number of PIMs you have to play with. Which, of course, reduces the dimensionality of the DM, leading to even reduced potential for confusion.