magic.magic.post.proc(X,object,w)magic after fitting the
model with model matrix X.magic, if one was.). If w is a vector then its
elements are typically proportional to reciprocal variances (but could even be negative)object contains rV (${\bf V}$, say), and
scale ($\phi$, say) which can be
used to obtain the require quantities as follows. The Bayesian covariance matrix of
the parameters is ${\bf VV}^\prime \phi$. The vector of
estimated degrees of freedom for each parameter is the leading diagonal of
${\bf VV}^\prime {\bf X}^\prime {\bf W}^\prime {\bf W}{\bf X}$
where $\bf{W}$ is either the
weight matrix w or the matrix diag(w). The
hat/influence matrix is given by
${\bf WX}{\bf VV}^\prime {\bf X}^\prime {\bf W}^\prime$
.The frequentist parameter estimator covariance matrix is ${\bf VV}^\prime {\bf X}^\prime {\bf W}^\prime {\bf WXVV}^\prime \phi$: it is sometimes useful for testing terms for equality to zero.
magic