R help - Dec 2010 - Estimate "between-axes" vs "within-axes heterogeneity of multivariate matrices

Hi!

My question(s) in the end might be silly but I am no expert on this, so here 
it goes:

Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered 
PCA being in some cases useful. They clearly explain that "it is the
case"
when multi-dimensional data display distinct clusters (which have zero, or 
near-zero, projections in some subset of the axes) and the task is (exactly) 
to separate this clusters among the principal components.

I have done my complete work using prcomp() and tested combinations of 
center=FALSE/TRUE and scale=FALSE/TRUE. I would like to now check this 
"between-axes" vs "within-axes" heterogeneity of my data and
cross-check
results with the various tested PCA-versions.

Is there any (official or custom) function available in R that could answer 
this question? Some relative/comparative (preferrable simple and intuitive) 
measure(s)? Something that would graphically perhaps give an indication 
without time-consuming clustering, sampling or whatsoever processing?

Even though the above mentoined authors mention some measure for the assymetry 
of the yielded compoenents ( uncentered -> unipolar, centered -> bipolar)
I
find the concept a bit hard to understand.

Isn't there a quick way (function) to just say (with numbers of plots of 
course) "well, it seems that the data are heterogenous looking at between-
axes" or  the other way around "it looks like the variables differ
within,
more than between"?

Apologies for repeating the same question (trying to understand the problem 
myself). Thank you, Nikos

Hi!

My question(s) in the end might be silly but I am no expert on this, so here 
it goes:

Noy-Meir (1973), Pielou (1984) and a few others have pointed to non-centered 
PCA being in some cases useful. They clearly explain that "it is the
case"
when multi-dimensional data display distinct clusters (which have zero, or 
near-zero, projections in some subset of the axes) and the task is (exactly) 
to separate this clusters among the principal components.

I have done my complete work using prcomp() and tested combinations of 
center=FALSE/TRUE and scale=FALSE/TRUE. I would like to now check this 
"between-axes" vs "within-axes" heterogeneity of my data and
cross-check
results with the various tested PCA-versions.

Is there any (official or custom) function available in R that could answer 
this question? Some relative/comparative (preferrable simple and intuitive) 
measure(s)? Something that would graphically perhaps give an indication 
without time-consuming clustering, sampling or whatsoever processing?

Even though the above mentoined authors mention some measure for the assymetry 
of the yielded compoenents ( uncentered -> unipolar, centered -> bipolar)
I
find the concept a bit hard to understand.

Isn't there a quick way (function) to just say (with numbers of plots of 
course) "well, it seems that the data are heterogenous looking at between-
axes" or  the other way around "it looks like the variables differ
within,
more than between"?

Apologies for repeating the same question (trying to understand the problem 
myself). Thank you, Nikos

On Wednesday 22 of December 2010 05:57:17 Nikos Alexandris wrote:
[...]
> Apologies for repeating the same question (trying to understand the problem
> myself).
I started to get a grip on this. But anyway, my questions are actually not 
directly about R questions - sorry for the traffic.

On Jan 6, 2011, at 10:03 PM, Nikos Alexandris wrote:
> Greets (again) :-)
>
> I finally ran mrpp tests. I think all is fine but one very important  
> issue: I
> have no idea how to export/save an "mrpp" object. Tried anything
I
> know and
> searched the archives but found nothing.
And what happened when you tried what seems like the obvious:

save(mrpp_obj, file=)
# rm(list=ls() )  # Only uncomment if you are ready for your workspace  
to clear
load("mrpp_store.Rdata")

>
> Any ideas? Is really copy-pasting the mrpp results the only way?
Many of us have no idea what such an object is, since you have not  
described the packages and functions used to create it. If you want an  
ASCII version then dput or dump are also available.


--
David Winsemius, MD
West Hartford, CT