calculate odds ratios for several predictors

[#1]

My data consists of two predictors, one with 3 responses (time at exposure) and 1 with two responses(gender.

I can do a log reg just fine and I cross my two variables, but I'm not sure how to interpret my OR's, .

I want to know: OR for early exposed females. How do I get that? Is that already in the table, or do I need multiply the OR for females with the OR for early exposed?

And if it's already in the table, how do I get early exposed males?

I've talked with my advisor and he insists that nominal is the way to go, so that's what I have done

[#2]

I might be wrong, but is sounds like you should include as a predictor the cross between time and gender.  It will have its own odds ratios which is what you are looking for.  It is entirely possible that you might be able to derive that from the odds ratios for the two predictors alone (although I doubt it), but I'm not smart enough to know how to do that.

[#3]

But I think I already did that? I have 3 model effects: Gender, Exposure status and gender*exposure status, and I don't get any ORs for that. I get parameter estimates and effect likelihood ratio for it though, that's why I thought I might be able to calculate it. It might also be a part of the given OR's I just can't figure it out.

This is the OR output, but where is female-late neutering against unaltered?

Level1	/Level2	Odds Ratio	Prob>Chisq	Lower 95%	Upper 95%
Late neutering	Early neutering	2.8559149	0.1924	0.5895623	13.834415
Unaltered	Early neutering	1.2280227	0.7489	0.3490645	4.3202328
Unaltered	Late neutering	0.4299927	0.1203	0.1482636	1.2470607
Early neutering	Late neutering	0.3501505	0.1924	0.0722835	1.6961735
Early neutering	Unaltered	0.8143172	0.7489	0.231469	2.8648004
Late neutering	Unaltered	2.3256207	0.1203	0.8018856	6.744742
Odds Ratios for Gender
Level1	/Level2	Odds Ratio	Prob>Chisq	Lower 95%	Upper 95%
Male	Female	1.1581507	0.789	0.3950657	3.3951648
Female	Male	0.8634455	0.789	0.2945365	2.5312246

[#4 · Accepted solution]

You are correct - it appears that JMP does not provide odds ratios for the interactions.  Further, it seems to be somewhat complex to calculate them, but it can be done.  I found the following to provide some detail:

http://www.ats.ucla.edu/stat/stata/seminars/interaction_sem/interaction_sem.htm

Also, the early slides in

http://www.cantab.net/users/filimon/cursoFCDEF/will/logistic_interact.pdf

appear to show what to do.

[#5]

Oh dear,
Well, thank you so much for those links, they're exactly what I need!