[R-sig-Geo] regression kriging in gstat with skewed distributions

[G. Allegri]

I'm trying to realize e regression kriging with gstat package on my
soil samples data. The response variable (ECe measuere) and covariates
appear positvely skewed.
As Tomislav Hengl suggests in its "framework for RK" [1], a logistic
transformation is proposed as a generic way to reduce the skeweness by
using the physical limits of the data.
Is it really a transformation that can be applied in the generic case
of skewed datas? I mean,in my case I have non-normal residuals (from
original data regression), and I'm trying to transform the residuals
(and not the original values) to do SK on them . Is this approach
correct?

A related question is how to do normal score transformations (for my
residuals) in R and gstat. I know gstat doesn't manage transformations
and back-transformations, so it should be done previously in R... but
I can't find any package that permit it in a straisghtforward way.
I've found something with qqnorm(ppoints(data)) and the approx()
function. Is that all?

Giovanni


[1] "A generic framework for spatial prediction of soil variables
based on regressionkriging" Geoderma 122 (1–2), 75–93.