cholInvArray {glmmBUGS} | R Documentation |
Given an array containing simulations from the posterior of a precision matrix, each individual precision matrix is converted to variances, covariances, and correlations.
cholInvArray(x, prefix = "T", chol=FALSE)
x |
An array of winbugs output, with precision matrix entries of the form "T[1,3]" |
prefix |
The name of the precision matrix in winbugs, the "T" in "T[1,2 ]" |
chol |
If TRUE, the cholesky decomposition is returned instead of the inverse |
Inverts the matrices with the cholesky decomposition, but operating on all matrices simultaneously using array arithmetic.
An array with the third dimension's precision matrix entries changed to
"sdT[i,i]" |
for the standard deviation of component i |
"covT[i,j]" |
for the covariance between i and j |
"corrT[i,j]" |
for the correlations between i and j |
# create a random positive definite matrix by # generating a lower triangle N=4 lmat = diag(runif(N, 1, 10)) thetri = lower.tri(lmat) lmat[thetri] = rnorm(sum(thetri), 0, 2) # precmat = solve(lmat %*% t(lmat)) precmat = solve(lmat %*% t(lmat)) # put this matrix into an array precarray = array(c(precmat), dim=c(1,1,length(precmat))) dimnames(precarray) = list(NULL, NULL, paste("T[", rep(1:N, N), ",", rep(1:N, rep(N,N)), "]",sep="") ) # invert it with cholInvArray and the solve function cholInvArray(precarray)[1,1,] # the off diagonals of solve(precmat) should be # the covT elements of cholInvArray(precarray) solve(precmat) # the standard deviations in cholInvArray(precarray) should be the # root of the diagonals of solve(precmat) sqrt(diag(solve(precmat)))