Imputation of multivariate panel or cluster data
Implementation of pan() that restricts the covariance matrix for the random effects to be block-diagonal. This function is identical to pan() in every way except that psi is now characterized by a set of r matrices of dimension q x q.
pan.bd(y, subj, pred, xcol, zcol, prior, seed, iter=1, start)
y |
See description for pan(). |
subj |
See description for pan(). |
pred |
See description for pan(). |
xcol |
See description for pan(). |
zcol |
See description for pan(). |
prior |
Same as for pan() except that the hyperparameters for psi have new dimensions. The hyperparameter c is now a vector of length r, where c[j] contains the prior degrees of freedom for the jth block portion of psi (j=1,...,r). The hyperparameter Dinv is now an array of dimension c(q,q,r), where Dinv[,,j] contains the prior scale matrix for the jth block portion of psi (j=1,...,r). |
seed |
See description for pan(). |
iter |
See description for pan(). |
start |
See description for pan(). |
A list with the same components as that from pan(), with two minor differences: the dimension of "psi" is now (q x q x r x "iter"), and the dimension of "last\$psi" is now (q x q x r).
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