# [R-sig-Geo] Back-transformation of Beta in geoR

Ole F. Christensen olefc at daimi.au.dk
Fri Feb 18 13:44:41 CET 2005

```Dear Ruben

You don't want to do kriging here.

I think the most simple solution is to this by simulation. Implicitly
you are saying that beta is one-dimensional.
You will find the mean and variance of the beta parameter from the
output from the likfit function.

mean(BCtransform(rnorm(2000, mean=outfromlikfit\$beta,
sd=sqrt(outfromlikfit\$beta.var)),lambda = 0.72, inverse = TRUE))

gives what you want.

Ole

Ruben Roa wrote:

>Hi:
>
>I am interested in back-transforming the mle of parameter Beta
>and its variance when the Lambda parameter of the Box-Cox
>transformation has been estimated and its estimate is not 0 nor
>0.5.
>Is this back-transformation equivalent to simply averaging over
>a fine grid inside the polygon containing the predictions of, say
>krige.conv (given that geoR back-transform when kriging=
>predicting)?
>For example
>
>
>>alpha<-sum(krig.object\$pred)/N
>>salpha<-sum(sqrt(krig.object\$krige.var))/N
>>
>>
>where krig.object has been obtained by using a likfit
>object as argument in krige.control, and N is the number of nodes
>in the grid (a big number).
>The questions are,
>1) is alpha aproximately equal to the back-
>transformation of the mle of Beta?
>2) is salpha aproximately equal to the 'standard
>error' of the back-transformation of the mle of Beta?
>
>Ruben
>
>_______________________________________________
>R-sig-Geo mailing list
>R-sig-Geo at stat.math.ethz.ch
>https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>
>
>
>

--
Ole F. Christensen
BiRC - Bioinformatics Research Center
University of Aarhus

```