Hei,
i have a species abundance data set CommData, with n (samples)=40 and p
(species)=107.
Sample Species A Species B Species C Species D ….
411_2010 40 20 0 0
412_2010 30 20 0 0
413_2010 0 0 0 0
414_2010 0 10 0 0
415_2010 20 0 0 0
418_2010 0 0 0 0
419_2010 0 0 0 0
421_2010 160 40 0 10
….
I try to find outliers based on the Mahalonis distance with the package
{mvoutliers}. I get an error using >aq.plot(CommData): "Error in covMcd(x,
alpha = quan) : n <= p -- you can't be serious!"
SoI try >pcout(CommData), which is supposed to work for high dimensions, but
get the error "More than 50% equal values in one or more variables!"
Can this be fixed? Any idea how i can find outliers in my multidimensional
data?
Thanks a lot for any help!!
--
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