Hi,
I am working myself with pollution data in soils and i have very high
values very close to very low values, and highly skewed
distribution. I am more and more concerned with doing kriging on
transformed data. This simply means we believe the data came
from only one population. But what if
Hi Ruben,
thanks so much for the references and especially the R
routines i will look into it. This may really give some good
answers to my data - once for all - i hope at least. I think we
neglect in majority of cases to verify if the data come from one or 2
(or more) distributions
Hello All
The common 'Normal Score' transform assumes one
population. Transformations such as rank or logarithm
do not assume one population.
The best way to identify likely mixtures is with
programs such as Peter MacDonald's Mix (cited in
Ruben's email I think):
Hello,
I agree that in many environmental datasets we could question the
assumption of existence of a single population. Although there are
ways to split the data into several populations, the key issue is
that the study area needs also to be stratified into several populations.
In some fields,
AH me, the English language slips away from me again.
I said that the PRESENCE {pardon the capitals, no way
to italicise email} of more than one population is
indicated by the points of inflexion on the
probability plot. Not that these were breakpoints
between populations.
Normal (or lognormal)
