Obtains Bayesian parameter covariance matrix, frequentist
parameter estimator covariance matrix, estimated degrees of
freedom for each parameter and leading diagonal of influence/hat matrix,
for a penalized regression estimated by magic
.
1  magic.post.proc(X,object,w=NULL)

X 
is the model matrix. 
object 
is the list returned by 
w 
is the weight vector used in fitting, or the weight matrix used
in fitting (i.e. supplied to 
object
contains rV
(V, say), and
scale
(s, say) which can be
used to obtain the require quantities as follows. The Bayesian covariance matrix of
the parameters is VV's. The vector of
estimated degrees of freedom for each parameter is the leading diagonal of
VV'X'W'WX
where W is either the
weight matrix w
or the matrix diag(w)
. The
hat/influence matrix is given by
WXVV'X'W'
.
The frequentist parameter estimator covariance matrix is VV'X'W'WXVV's: it is sometimes useful for testing terms for equality to zero.
A list with three items:
Vb 
the Bayesian covariance matrix of the model parameters. 
Ve 
the frequentist covariance matrix for the parameter estimators. 
hat 
the leading diagonal of the hat (influence) matrix. 
edf 
the array giving the estimated degrees of freedom associated with each parameter. 
Simon N. Wood simon.wood@rproject.org
magic
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