[R-sig-Geo] question about regression kriging

[ONKELINX, Thierry]

Dear David,

An other option would be to use sequential gaussian simulation. That
will allow to calculate confidence intervals in the logit scale. These
can be back-transformed into the original scale because the logit
transformation is monotone. 

HTH,

Thierry

------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx at inbo.be 
www.inbo.be 

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than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-sig-geo-bounces at stat.math.ethz.ch
[mailto:r-sig-geo-bounces at stat.math.ethz.ch] Namens Edzer Pebesma
Verzonden: dinsdag 8 april 2008 20:50
Aan: David Maxwell (Cefas)
CC: r-sig-geo at stat.math.ethz.ch
Onderwerp: Re: [R-sig-Geo] question about regression kriging

David Maxwell (Cefas) wrote:
> Hi,
>
> Tom and Thierry, Thank you for your advice, the lecture notes are very
useful. We will try geoRglm but for now regression kriging using the
working residuals gives sensible answers even though there are some
issues with using working residuals, i.e. not Normally distributed,
occasional very large values and inv.logit(prediction type="link" +
working residual) doesn't quite give the observed values.
>
> Our final question about this is how to estimate standard errors for
the regression kriging predictions of the binary variable?
>
> On the logit scale we are using
>  rk prediction (s0) = glm prediction (s0) + kriged residual prediction
(s0) 
> for location s0
>
> Is assuming independence of the two components adequate?
>  var rk(s0) ~= var glm prediction (s0) + var kriged residual
prediction (s0) 
>   
In principle, no. The extreme case is prediction at observation 
locations, where the correlation is -1 so that the final prediction 
variance becomes zero. I never got to looking how large the correlation 
is otherwise, but that shouldn't be hard to do in the linear case, as 
you can get the first and second separately, and also the combined using

universal kriging.

Another question: how do you transform this variance back to the 
observation scale?
--
Edzer

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