-------- Original Message --------
Subject: RE: Fwd: CVA versus MANOVA
Date: Tue, 22 Mar 2011 17:34:50 -0700
From: Sarah Degroot <[email protected]>
To: <[email protected]>
In SPSS you can use the "leave one out classification" option for
cross-validation.
Frequently I also randomize my data and re-run the analysis with the
leave-one-out classification, just to see if the actual data groups
cases better than random data.
Sarah De Groot
Graduate Student, Rancho Santa Ana Botanic Garden and Claremont Graduate
University
________________________________
From: morphmet [mailto:[email protected]]
Sent: Mon 21/03/2011 5:44 AM
To: morphmet
Subject: Re: Fwd: CVA versus MANOVA
-------- Original Message --------
Subject: Re: Fwd: CVA versus MANOVA
Date: Mon, 21 Mar 2011 08:03:00 -0400
From: Øyvind Hammer <[email protected]>
To: <[email protected]>
Hi, this is normal ... if you include a large number of variables
compared with cases in the CVA, you will see a strong separation even
for completely random data with no real groups. The MANOVA, on the other
hand, will adjust for the number of variables, and (correctly) report
non-significance.
In such cases, you will see that the seemingly successful
classification breaks down completely if you run a cross-validation
(jack-knifing) on the CVA.
A CVA should always be accompanied by a MANOVA to check that the groups
are "real".
Or something like that.
Øyvind Hammer
Natural History Museum
University of Oslo
Hi,
I am analyzing a dataset with 21 landmarks and 2 groups. I have run
CVA
on both IMP and MorphoJ and it suggests a very strong grouping. The
deformation plot in IMP shows 3 areas of deformation while the
MorphoJ
plot doesn't show any obvious deformations on CV1 (the significant
axis
as displayed by IMP). However when I ran a univariate ANOVA on the
centroid sizes and a MANOVA on the partial warps and relative warps
with
SPSS there was no significant difference between groups.
I am new to this type of analysis and I was wondering if perhaps I am
missing something?
Any suggestions would be greatly appreciated.
Thanks,
Michelle
