Odds ratio SE for interaction term

questions concerning analysis/theory using program MARK

Odds ratio SE for interaction term

Postby mparren » Thu Sep 12, 2019 6:11 pm

EDIT: Since researching the topic more, I now realize that I cannot simply exponentiate the log odds standard error to get standard error for the odds ratio. I think I will probably just use the log odds confidence intervals given and work with those instead.

Hi all-

I have seen several postings related to this but wanted to confirm my understanding of how to calculate the odds ratio and SE for an interaction term between a binary covariate and a continuous covariate in single season occupancy modeling (logit link).

Intercept = beta 1 (b1)

I have year as a covariate = 0 or 1 (b2);
A measure of human disturbance (HD) which is continuous (b3);
And an interaction term of year and HD (b4)

I believe that the odds ratio for HD in year 0 = e(b3)
and the odds ratio for HD in year 1 = e(b3+b4)

Because the odds ratio for HD in year 0 = e(b3), I used the SE for that covariate and did e(SE of b3) for the odds ratio SE.

The SE for the odds ratio of HD in year 1 is where I am less confident:
I printed the variance-covariance matrix for the betas and calculated the SE for the interaction in year 1:
e(sqrt(var(b3)+var(b4)+2*covar(b3,b4))).

Am I understanding this correctly?

Thanks so much!

- Molly
mparren
 
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