Hello everyone,
at the moment I'm using the tgp package for modelling a nonstationary
data set on a two dimensional area D and I'm interested in the
prediction and the estimated covariance matrix. For this purpose I'm
using the function btgp. As far as I understand, btgp uses a MCMC
algorithm to split up D along lines parallel to the coordinate axes and
estimates independent Gaussian processes on each resulting region,
conditional on the predefined class of covariance functions (like the
Matern class) and returns a tgp class object, which I call 'out'.
Now I'm wondering if out$Zp.s2 gives the estimated covariance matrix. In
my example I have a matrix of locations X and data set Z. When I run the
btgp function it yields a tree of hight two, indicating that the
algorithm splits up the area D into two independent regions and
correspondingly the matrix of locations X. But if I look at out$Zp.s2
the matrix has no zeros, which implies dependence of both regions. So I
don't understand what Zp.s2 gives exactly and how I can get the
estimated covariance matrix.
A similar question is what ZZ.s2 gives? The manual says that this yields
the predictive covariance matrix at some predictive locations XX. But if
I put X=XX it doesn't yield the same as Zp.s2.
So I would be grateful if someone could help me.
Best regards
Jochen Fiedler
