[Eeglablist] PCA eigenvalues

[Arnaud Delorme]

To obtain the eigenvalue from the decomposition, you should use the 
runpca() function or the matlab svd() function. For instance, from an 
EEGLAB dataset

[pc,eigvec,sv] = runpca(EEG.data(:,:));

% Outputs:
% pc - the principal components, i.e. >> inv(eigvec)*data = pc;
% eigvec - the inverse weight matrix (=eigenvectors). >> data = eigvec*pc;
% sv - the singular values (=eigenvalues)

You may obtain the percentage variance accounted for by using the 
compvar() function (enter PCA components instead of ICA components), 
i.e. to get the percentage accounted for by the first PCA component

[proj pvaf] = compvar(EEG.data(:,:), pc, eigvec, 1);
% pvaf = percentage of variance accounted for

Arno

Christopher Summerfield wrote:

>hi there
>
>when using the 'pca' option in runica(), is it possible to determine the
>eigenvalues/ %variance accounted for by each factor extracted?
>
>thanks
>chris summerfield
>
>
>
>
>
>Christopher Summerfield
>summerfd at psych.columbia.edu
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-- 

*Arnaud Delorme, Ph.D.*
Computational Neurobiology Lab, Salk Institute
10010 North Torrey Pines Road
La Jolla, CA 92037 USA

*Tel* : /(+1)-858-458-1927 ext 15/
*Fax* : /(+1)-858-458-1847/
*Web page *: www.sccn.ucsd.edu/~arno <http://www.sccn.ucsd.edu/%7Earno>


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