从mcmc对象重构变量

Question

I am using rjags as a sampler. The model has 3 matrices defined. The coda.samples function returns a list of samples. If I take the first sample list the column names look something like this:

> colnames(output[[1]])
"A[1,1]"  "A[2,1]"  "A[1,2]"  "A[2,2]" ... 
"B[1,1]"  "B[2,1]"  "B[3,1]"  "B[4,1]" ... 
"C[1,1]"  "C[2,1]"

Obviously, A, B and C are matrices in my model. I want to reconstruct them based on the mean of these samples. I can easily get the means with colMeans(output[[1]]) but I have no idea how to easily reconstruct the matrices from this vector.

A good way for reconstruction would be the relist() function. So If I had matrices A, B and C in a list L = list(A=A,B=B,C=C) then I could transform this list to a vector with unlist() and convert back with relist() . I am looking for something similar/readymade for mcmc objects, but without avail so far - I can't believe I am the first one needing this. Obviously, relist(colMeans(output[[1]])) doesn't work.

Anyone can help me with reconstructing?

Edit: note also that the relist() function only needs a skeleton, so extracting the skeleton from colnames(output[[1]]) would also do the trick. Or am I complicating?

Answer 1

I don't think relist() will do the trick...

I assume your object output is an object of class mcmc.list , as defined in the R package coda , and output[[1]] is an object of class mcmc representing the sample for the first MCMC chain.

I'm pretty sure coda does not have any understanding that eg "A[1,1]" is a JAGS matrix, it just handles it as a variable name. So you'll have to iterate over the relevant variables and impose the structure yourself.

Ideally you'd wrap this in a function like the following:

getMatrix <- function(output, varname, rows, cols) {
  unname(
    sapply(1:cols, function(j)
      sapply(1:rows, function(i)
        summary(output[,sprintf("%s[%s,%s]", varname, i, j)])[[1]][1]
      )
    )
  )
}

Thus, for example, if your matrix B stored in output[[1]] has 3 rows and 4 columns you'd write:

getMatrix(output[[1]], "B", 3, 4)

to get the means as a matrix object in R.