Well I am working on slowly incorporating a more Bayesian approach in order to get away from NHST. I am running a Bayesian logistic model with one binary predictor. Literature states (I believe) my intercept and independent variable should have a normal prior.
The non-informative (flat) prior is mean = 0 and variance = 1,000ish, which to my knowledge when I apply the logistic function they translate to mean = 0.5 and var = 1.
Now for my informative prior: mean = 1 and var = 0.5, as an example, which translate to mean = 0.7 and var = 0.62.
Am I correct in presuming these are the equivalent of the beta coefficient values or are they on the probability scale now (which seems more likely), so not log odds yet. Thus if I exponeniate them I get a mean odds ratio of 2 and precision of 1.87 or do I p/(1-p) and then exponentiate? I am just trying to make sure I am using the priors that I thought I was!!
Thanks you.
Does my above thoughts seem correct or am I missing anything?
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No, I agree. I thought the same thing when I wrote that word. I felt like I was just throwing it out there because I had seen it so many times.
"Weak" was likely the term I was thinking of!
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Just saw this in a Gelman et al. paper I am reading:
"Setting the scale s to infinity corresponds to a flat prior distribution"
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It seriously took me 7 hours of reading and combing the web before I found a straightforward interpretation for my question. The mean value is your beta coefficient and the following helps you get a variance value. The latter part was what most of the searching was in reference to.
http://documentation.sas.com/?docset...14.2&locale=en
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