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2013-03-17T19:42:28.373
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You want to get your data into a data frame instead of a matrix, and then use the formula interface to <code>aggregate</code>. Ideally you want to get your MCMC output in a form that you can read directly into a data frame, but if you are stuck with the matrix, then use <code>melt</code> or <code>reshape</code> + <code>as.data.frame</code> or just do something like this (assuming you have a matrix called <code>M</code> with the three dimensions discussed above): <pre><code>d<-data.frame(year=rep(1991:1995,each=40), agency=rep(c("D","O","T","R"),50), iteration=rep(0:9,5,each=4), spend=as.vector(M)) </code></pre> in order to get a data frame that looks like this: <pre><code> year agency iteration spend 1 1996 D 0 49.184181 2 1996 O 0 4.351598 3 1996 R 0 16.051958 4 1996 T 0 -14.896019 5 1996 D 1 45.910362 6 1996 O 1 2.173807 7 1996 R 1 17.161775 ... </code></pre> Now you can use <code>aggregate</code> to apply your function, like this: <pre><code>aggregate(spend~agency+year,d,myfunc) </code></pre> to get <pre><code> agency year spend.V1 spend.2.5% spend.97.5% 1 D 1996 35.380610 3.989422 54.098005 2 O 1996 2.634854 -2.700893 8.223760 3 R 1996 13.607076 3.737874 17.111344 4 T 1996 -32.289610 -66.065034 -4.669537 5 D 1997 37.588003 4.231116 57.039164 6 O 1997 10.818397 3.755926 16.918627 ... </code></pre> and now you can slice and dice as you wish <pre><code>aggregate(spend~year,d,myfunc) aggregate(spend~agency,d,myfunc) etc... </code></pre>
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