This function loops over each community and then loops over each group member, fitting a Bayesian multivariate (bivariate in this case) normal distribution to each group of data. Not intended for direct calling by users.
siberMVN(siber, parms, priors)A list of length equal to the total number of groups in all
communities. Each entry is named 1.1 1.2... 2.1.. with the first number
designating the community, and the second number the group within that
community. So, 2.3 would be the third group within the second community.
Each list entry is a 6 x n matrix representing the back-transformed posterior
distributions of the bivariate normal distribution, where n is the number of
posterior draws in the saved sample. The first two columns are the back-
transformed means, and the remaining four columns are the covariance matrix
Sigma in vector format. This vector converts to the covariance matrix as
matrix(v[1:4], nrow = 2, ncol = 2).
a siber object as created by createSiberObject()
a list containing four items providing details of the
rjags::rjags() run to be sampled.
n.iter The number of iterations to sample
n.burnin The number of iterations to discard as a burnin from the
start of sampling.
n.thin The number of samples to thin by.
n.chains The number of chains to fit.
a list of three items specifying the priors to be passed to the jags model.
R The scaling vector for the diagonal of Inverse Wishart
distribution prior on the covariance matrix Sigma. Typically
set to a 2x2 matrix matrix(c(1, 0, 0, 1), 2, 2).
k The degrees of freedom of the Inverse Wishart distribution for
the covariance matrix Sigma. Typically set to the dimensionality of Sigma,
which in this bivariate case is 2.
tau The precision on the normal prior on the means mu.