MortalitySmooth
which
calculates the inner product of a matrix (from a Kronecker product)
and a sparse weight matrix in order to obtain standard errors. It
uses the same idea employed in MortSmooth.BWB
and the elements
after the IWLS converged, including the penalty term.
Mort2Dsmooth_se(RTBx, RTBy, nbx, nby, BWB.P1)
predict.Mort2Dsmooth
when standard errors are required. The arguments BWB.P1
is the
LHS after convergence is reached and smoothing parameter selected. The
standard errors as given in the function are computed for the linear
predictor term and simple computation is needed to obtain standard
errors for the Poisson counts. Anyway
predict.Mort2Dsmooth
takes care of such differences.
The Generalized Linear Array Models setting is explained in the
reference in MortSmooth_BWB
and
Mort2Dsmooth
.
Mort2Dsmooth
, MortSmooth_BWB
,
predict.Mort2Dsmooth
.