Hi Martin, 

Certainly there are some problems with the "ken.sto" function.... In
addition there are some considerations that you have to take into account
before using this code. For instance, it projects the data onto a principal
component space prior sampling. In this case you have to a PCA is really
necessary for your data. Second, this function uses the Euclidean distance
(ED) as metric for selecting the samples. By using the ED directly on the
PCs, the PCs with high explained variance will “dominate” the measurements.
In this case is better to standardize the PCs prior ED computation or use
the Mahalanobis distance.
Whatever, I have modified the “ken.sto” code and now seems to be that it
works for your data ;-) . If you still need it just drop me an e-mail.
Regards, 

Leonardo Ramirez-Lopez, 

Researcher
Georges Lemaître Centre for Earth and Climate Research
Earth and Life Institute
Université Catholique de Louvain,
3 Place Louis Pasteur
1348, Louvain la Neuve
Belgium
e-mail: leonardo.rami...@uclouvain.be

http://www.uclouvain.be/en-teclim.html


Ph.D Researcher
Physical Geography and Soil Science
Institute of Geography
University of Tübingen
Rümelinstr. 19-23
72070 Tübingen
Germany
http://www.geographie.uni-tuebingen.de/ 



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