Thank you for the explanation Dr Philipp.

On Thursday, May 25, 2017 at 10:16:06 PM UTC+8, mitt...@univie.ac.at wrote:
>
> 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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