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TODO
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TODO
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o plot.lokern() in ~/Vorl/WBL-Nonpar_Regr/R/lokerns-ex.R allows to plot __bandwidth__ h
------------ *BUT* it shadows the plot.KernS() method in the package
~~~~~~~~~~~~
==> define plot.lokerns() and it *calls* plot.KernS() but additionally *optionally* visualizes the bandwidth
o Now both lokerns() and glkerns() do return class()ed results which
have print(), residuals(), ... methods.
Both now also use
sfsmisc::seqXtend() {-> sfsmisc:roundfixS() }
--> dependency on sfsmisc
[[Alternative: Write small paper on seqXtend() & roundfixS()
and move these to utils ]]
o The Fortran-algorithm in parts heavily relies on t_i < t_{i+1}
When we have *duplicated* x's [or "nearly-duplicated" ones which may
be even more subtle ! --- there maybe numerical problems that we should
deal with.
The smooth.spline() - like solution requires an algorithm that works
with *weights* ... and that maybe much too much work, needing more
theoretical work, first !
o Both functions now return lists almost identical;
this is nice and useful.
o The 2 man pages look very similar; ``clean up''
Done:
=====
o the nice plots in tests/glk1.R now in demo/glk-derivs.R
o User [R.V.]: glkerns() and lokerns() should return an object for which I can
predict(*, deriv = d,...) {d = 0, 1, 2, ..}
^^^^^^^^^ .. however that would require that
"nu - k" := deriv - korder could be non-even etc
--> Would need more "research"; see comment in src/glkerns.f
o plot(.) method works, also when object had deriv=1,..
o residuals() and fitted() give an error correctly, when 'x.out' is not
ok, i.e. (gl|lok)kerns(..) did not include x.inOut = TRUE {which
however is default, so the user should know what he is doing...}