Dear Thierry,
the 'mefa' package should do this, and I am also interested in the
testing of the package for such a large number of species. I have used
it before with 75K records, but only with ~160 species and 1052 sites.
So please let me know if it worked!
You can do the clustering like this
This method for converting long to wide format seems to work well with
pretty large datasets and it uses only base functions.
# this function will return a site*species matrix
# based on the formula variable. Data does not need
# to be grouped, the xtabs function will take care of
# summing
Thanks for the illustration of xtabs.
A quibble: Doesn't the following work, substituting as.matrix() for
matrix()?
(Does seem to conserve the dimensions and dimension names.)
matrify-function(datatable, formula = units~site+spp, relativize=F){
tbl-xtabs(formula,data=datatable)
mx
I've recently been engaged in some exploratory data analysis also involving
cluster analysis, albeit on a much smaller dataset. There are quite a few
packages (e.g. ecodist(), vegan(), pvclust()) that include functions for
undertaking cluster analysis, but have you, or anyone else on here
Thats great, thanks. I always like it when someone can suggest a better
or cleaner way to do something in code.
-Chris
[EMAIL PROTECTED] wrote:
Thanks for the illustration of xtabs.
A quibble: Doesn't the following work, substituting as.matrix() for
matrix()?
(Does seem to conserve the
Hi,
I have searched in archives to looking for a library (or script, or method) for
to evaluate a mortality analysis.
I test different drugs (9 test, 1 positive control, and 1 negative) on 30
insects larvae (each ones larvae in one coffee plastic cup). This one was
repeated 4 times. After five
