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. _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
