(This article was originally published at Doing Bayesian Data Analysis, and syndicated at StatsBlogs.)
First, I changed the hyperprior on the variance (standard deviation) parameters. I was never pleased with the "+.1" kludge that I used in the book, explained on p. 496: "There is one other trick in the BUGS model specification that is not in the hierarchical diagram of Figure 18.1. One line of the BUGS model specifies that the standard deviation of the group effects, denoted aSDunabs, comes from a t distribution: aSDunabs ~ dt(0,0.001,2). Another line takes the absolute value to “fold” the t distribution onto the nonnegative numbers: aSD <- abs(aSDunabs) + .1. But that line also mysteriously adds a small constant, namely 0.1. This constant keeps aSD from venturing extremely close to zero. The reason for keeping aSD away from zero is that shrinkage can become overwhelmingly strong when there are many groups with few data points per group. This becomes especially problematic in the next chapter when we consider interaction of factors." A problem with that kludge is that the aSD parameter can never be less than 0.1, which is unlikely to reflect a real prior belief. It can produce strangely truncated posterior distributions. Therefore I simply abandoned the folded-t prior (recommended by Gelman, 2006) and used a reasonable gamma instead. The gamma prior, for each SD parameter, has a mode at 0.1 and a standard deviation of 10, which seems to be an appropriate scaling when the data are standardized (as indeed they are in the programs). Here's a picture of this prior:
Second, I changed the plots of the variance parameters so that they are on the original data scale, not the standardized scale. This was achieved merely by multiplying by ySDorig.
Third, I changed how contrasts are specified. The original method was problematic because the contrasts were specified as vectors of contrast coefficients, which (a) lacked explicit meaning and (b) only made sense if the order of the levels of the factors was exactly as assumed by the contrast coefficients. Unfortunately, it turns out that different settings of R's "locale" can produce different orderings of the factor levels, rendering the contrast coefficients nonsensical! (Thanks to Damion Junk for pointing out this problem to me.) The new method uses logical indexing of named levels, so that the contrast specification is meaningful and independent of local ordering conventions. For example, for the mussel muscle data, the old contrast specification was
contrastList = list( ...
ORE1vORE2 = c(1,-1,0,0,0) ,
ALAvORE = c(-1/2,-1/2,1,0,0) ,
... )
but the new specification is
normalize = function( v ){ return( v / sum(v) ) }
contrastList = list( ...
ORE1vORE2 = (xnames=="OregonN")-(xnames=="OregonT") ,
ALAvORE = (xnames=="Alaska")-normalize(xnames=="OregonN"|xnames=="OregonT") ,
... )
Fourth, I improved the program for displaying posterior histograms, so that it now defaults to better breaks based on the HDI. Therefore it is best to use plotPost without a breaks argument unless the (new) default looks bad.
Where are the new programs? Get the updated files, ANOVAonewayJagsSTZ.R, ANOVAtwowayJagsSTZ.R, plotPost.R, and McDonaldSK1991data.txt, from the usual place (click the column labeled "Last Modified" to get the most recent listed first). ** Updated April 25, 2012. Also with the program PoissonExponentialJagsSTZ.R **
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