Description Usage Arguments Details Value Examples
This function generates a precision matrix for a general 3-level Gaussian hierarchical model (level 0 being the root, and level 4 being the observations) given the variances for each level tau, tau_a, tau_b, tau_c, and sigma_2. It takes advantage of the sparse structure of the precision matrix for centered 2-level gaussian hierarchical models and returns only the non-zero entries of the precision matrix together with its corresponding indices.
1 2 |
i |
number of nodes at level 1 |
J |
vector specifying the number of children nodes in level 2 per node at level 1 |
K |
vector specifying the number of children nodes in level 3 per node at level 2 |
flat_prior |
determines whether to use the density with flat prior |
tau |
variance of the root (level 0) |
tau_a |
variance for parameters in level 1 |
tau_b |
variance for parameters in level 2 |
tau_c |
variance for parameters in level 3 |
sigma_2 |
variance of the observations |
Assumptions: - variances are constant for parameters within the same level - all observations y_ijk are equal to 0 - the mean of the root parameter B is mu = 0
list of non-zero entries of the precision matrix together with its corresponding indices
1 2 3 4 |
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