Banova

Mark Bucciarelli writes:

I’m interesting in applying the Bayesian ANOVA you described in your 2005 paper to some data I am analyzing.

Is your arm package for R the place to start? (I’m on Linux/Mac, so I’ll have to build OpenBugs and maybe update the RBugs package.)

Or is there a more direct path?

I’m analyzing the impact of ad attributes on the variance in click rates; e.g., product category, time of day, graphical vs. text, etc, etc.

My reply: For most of the computations in that article, I actually used a Fortran program that I wrote (and ran from Splus). There’s no way this could be recovered; it would be easier to start from scratch. For the last example (in Figures 6 and 7), I used Bugs; actually, I repeated this example in my book with Hill. For your example, I’d suggest Bugs if your sample size isn’t too large. Or you could try Doug Bates’s lmer() function which we use in our arm package. lmer doesn’t currently express uncertainty in the variance parameters but it’s a good start, certainly much better than trying to use R’s aov() function or similar procedures in other packages.

4 thoughts on “Banova

  1. lmer doesn't currently express uncertainty in the variance parameters

    In R2.9-0 VarCorr(mcmcsamp(lmerModel, 10), "varcov") works (OK, with >10 iterations).

    I'm about to listen to Martyn Plummer talk, so I feel obliged to point out that JAGS will also work. :-)

  2. The database community has policies about this. See http://tods.acm.org/Authors.html#ManuscriptPrepar

    In particular, they say "The solution is to reference your prior published work in the third person (just as you would any other piece of work that is related to the submitted paper). This allows you to set the context for the submitted paper, while at the same time preserving anonymity."

    They also have thought about some refinements.

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    The authors have also developed closely related techniques for query optimization [Anonymous], but…

    In the above example, the reference in the bibliography would then read:

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