[R-sig-Geo] question about regression kriging

[Edzer Pebesma]

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