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From: RFisher on 17 May 2010 15:39
Hi everyone,
I'm working with a dataset on nest use of a small grassland songbird.
What I've done is to develop models of nest use (0=nest, 1=random)
using 5 explanatory variables (i.e., variables related to vegetation
structure) in a logistic regression framework. Using AIC I've found my
top models and have used model-averaging to determine model-averaged
parameter estimates and associated standard errors. I've then used
these model-averaged parameter estimates to determine predicted
probabilities of nest use based on each variable individually, while
holding the other variables constant (at average values). For example,
I wanted to calculate the predicted probability of nest use at
vegetation heights of 5cm, 10cm, etc.. But I can't figure out how to
calculate SEs around those predicted probabilities. Is there a
specific formula, is there a way to do this directly in SAS? Note,
I've had to calculate the predicted probabilities by hand since I
can't seem to figure out how to do it in SAS using model-averaged
parameter estimates and not simply the parameter estimates from the
"best" model.
I hope that is sufficient information!
Thanks,
Ryan
From: Bminer on 20 May 2010 17:58
On May 17, 3:39 pm, RFisher <ryanfis...(a)sasktel.net> wrote:
> Hi everyone,
>  I'm working with a dataset on nest use of a small grassland songbird.
> What I've done is to develop models of nest use (0=nest, 1=random)
> using 5 explanatory variables (i.e., variables related to vegetation
> structure) in a logistic regression framework. Using AIC I've found my
> top models and have used model-averaging to determine model-averaged
> parameter estimates and associated standard errors. I've then used
> these model-averaged parameter estimates to determine predicted
> probabilities of nest use based on each variable individually, while
> holding the other variables constant (at average values). For example,
> I wanted to calculate the predicted probability of nest use at
> vegetation heights of 5cm, 10cm, etc.. But I can't figure out how to
> calculate SEs around those predicted probabilities. Is there a
> specific formula, is there a way to do this directly in SAS? Note,
> I've had to calculate the predicted probabilities by hand since I
> can't seem to figure out how to do it in SAS using model-averaged
> parameter estimates and not simply the parameter estimates from the
> "best" model.
>  I hope that is sufficient information!
> Thanks,
> Ryan

Ryan,

No help but very interested. How did you calculate the parameter
coefficients and SE under model averaging? Then do you create a CI
using a normal approximation? Love if you can share how you calculated
them.

Brian

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