Note: to give statistically reliable results it needs to do more than just 
“culls the dimensions of x to match the number of positive non-zero 
eigenvalues”. You need to have the number of degrees of freedom much larger 
than the number of dimensions of the space or else you are likely to find that 
the PC dimensions with the smallest eigenvalues apparently account for most of 
the differences among groups. Simply using randomization tests do not solve 
this problem.

 

____________________________________________

F. James Rohlf, Distinguished Professor, Emeritus. Ecology & Evolution

Research Professor, Anthropology

Stony Brook University

 

From: Collyer, Michael [mailto:michael.coll...@wku.edu] 
Sent: Tuesday, April 5, 2016 5:29 AM
To: Brenna Hays <bkhays...@gmail.com>
Cc: MORPHMET <morphmet@morphometrics.org>
Subject: Re: [MORPHMET] replacing df with principal components for MANOVA tests

 

Brenna, 

 

The PCA you performed produces a list of objects, one of which is a matrix of 
PC scores.

 

Change AIS.PC to AISPC$x, and it should work.  In this case, $x is the matrix 
of scores.

 

The help files for the functions you use tell you the objects that are 
returned.  You can also use attributes() to see the list of objects returned.  
The $ indicate that you are going to the list and extracting the object that 
follows.  

 

Just as a cautionary note, you might make sure that the number of columns for x 
is not the same as the number of landmarks times the dimensions (2 or 3).  I 
cannot remember if R automatically culls the dimensions of x to match the 
number of positive non-zero eigenvalues.  If not, you might have to do that 
yourself.  There are also non-parametric MANOVA options that do not rely on 
degrees of freedom, which would make theses steps unnecessary, if you are 
comfortable with using randomization tests.

 

Good luck!

Mike

 

Michael Collyer

Associate Professor

Biostatistics
Department of Biology
Western Kentucky University
1906 College Heights Blvd. #11080 
Bowling Green, KY 42101-1080
Phone: 270-745-8765; Fax: 270-745-6856
Email: michael.coll...@wku.edu <mailto:michael.coll...@wku.edu> 

 

On Apr 5, 2016, at 10:16 AM, Brenna Hays <bkhays...@gmail.com 
<mailto:bkhays...@gmail.com> > wrote:

 

I have been reading up on MANOVA and come across the common complication 
requiring a large number of degrees of freedom. After performing Procrustes 
superimpositions on my landmark configurations, along with my small sample 
sizes, is not allowing me to perform MANOVA tests. 

 

I have read that one can fix this problem by replacing the coordinates with 
principal components. The coding from my information source is very vague, but 
I've been trying some different things. Below is my code - note the error 
message at the bottom. 

 

AISarray <- two.d.array(AIS.shape)

 

ID <- c(1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3)

Depth <- c(3, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5)

Region <- c(7, 8, 8, 8, 8, 8, 8, 8, 5, 5, 5)

RR <- c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)

CDR <- c(2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)

 

AISdf <- cbind(AISarray, ID, Depth, Region, RR, CDR)

 

specs <- as.factor(AISdf[,51])

depth <- as.factor(AISdf[,52])

region <- as.factor(AISdf[,53])

rr <- as.factor(AISdf[,54])

cdr <- as.factor(AISdf[,55])

 

AIS.PC <- prcomp(AISarray)

summary(manova(lm(AIS.PC~specs*region)))     #NOT WORKING

Error in model.frame.default(formula = AIS.PC ~ specs * region, 
drop.unused.levels = TRUE) : 

  invalid type (list) for variable 'AIS.PC'

 

It seems MANOVA requires a data frame, but replacing the coordinates with 
principal components turns it into a list. I cannot find any other information 
on this matter. Any help would be much appreciated. 

Thanks, 

 

Brenna Hays

Research Assistant, Master's Student

Nova Southeastern University

 

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