Nobody answered my first request. I am sorry if I did not explain my
problem clearly. English is not my native language and statistical
english is even more difficult. I'll try to summarize my issue in
more appropriate statistical terms:
Each of my observations is not a single number but a vector of 5
proportions (which add up to 1 for each observation). I want to
compare the "shape" of those vectors between two treatments (i.e. how
the quantities are distributed between the 5 values in treatment A
with respect to treatment B).
I was pointed to Hotelling T-squared. Does it seem appropriate? Are
there other possibilities (I read many discussions about hotelling
vs. manova but I could not see how any of those related to my
particular case)?
Thank you very much in advance for your insights. See below for my
earlier, more detailed, e-mail.
On 2007-May-21 , at 19:26 , jiho wrote:> I am studying the vertical distribution of plankton and want to
> study its variations relatively to several factors (time of day,
> species, water column structure etc.). So my data is special in
> that, at each sampling site (each observation), I don't have *one*
> number, I have *several* numbers (abundance of organisms in each
> depth bin, I sample 5 depth bins) which describe a vertical
> distribution.
>
> Then let say I want to compare speciesA with speciesB, I would end
> up trying to compare a group of several distributions with another
> group of several distributions (where a "distribution" is a
vector
> of 5 numbers: an abundance for each depth bin). Does anyone know
> how I could do this (with R obviously ;) )?
>
> Currently I kind of get around the problem and:
> - compute mean abundance per depth bin within each group and
> compare the two mean distributions with a ks.test but this
> obviously diminishes the power of the test (I only compare 5*2
> "observations")
> - restrict the information at each sampling site to the mean depth
> weighted by the abundance of the species of interest. This way I
> have one observation per station but I reduce the information to
> the mean depths while the actual repartition is important also.
>
> I know this is probably not directly R related but I have already
> searched around for solutions and solicited my local statistics
> expert... to no avail. So I hope that the stats' experts on this
> list will help me.
>
> Thank you very much in advance.
JiHO
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