As an exploratory technique, PCA makes no distributional assumptions; it is 
used to explore the empirical distribution of the data. The sample does not 
need to be balanced with regard to sex or other grouping variables, but 
larger groups have a stronger effect on the PCA than smaller groups.

The origin of the coordinate system is arbitrary. However, many software 
packages center the data so that the origin (i.e. where the axes intersect) 
equals the mean value. 

Am Donnerstag, 25. Mai 2017 09:58:31 UTC+2 schrieb Helmi Hadi:
> Dear morphometricians, 
> Does a sample need to be normally distributed when conducting PCA in 
> geometric morphometrics? Sometimes due to research constraints there are no 
> samples of the opposite sex. Someone was asking me this question, and I do 
> not have the answer. When I look at the data distribution, there is quite 
> an imbalance male/female population. However, the classifiers male/female 
> and species are there and you can sort of tell which group belongs to 
> where. My only fear is that the confidence ellipse for the males are being 
> "gravitated" towards the females for one species as that species does not 
> have any male specimens. Attached are the file which I have recreated the 
> dataset based on memory. 
> Is this kind of data acceptable or publishable? 
> My own personal question is based on the GMM results given in MorphoJ. The 
> PC1/PC2 axes does not intersect at the middle (which I have personally 
> drawn the dotted line there). I don't mind this output, but does it matter 
> to have the axes cut at the 0 value? The data data distribution does not 
> change with the change of axes lines. I noticed some GMM papers have the 
> axes at 0. 
> Thanks all for the help,
> Helmi Hadi,
> School of Health Scienes, 
> Universiti Sains Malaysia

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