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

>

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>

>

>

--

Ole F. Christensen

BiRC - Bioinformatics Research Center

University of Aarhus