As far as I understand, in pca, the scores are equivalent to the eigenvectors of the covariance matrix.
I am working with a matrix called realizations, which is 144*5, meaning there are 144 observations of 5 random variables.
If I do the pca as
[coeff,score,latent,tsquared,explained,mu] = pca(realizations);
I obtain as score a matrix of size 144*5, as expected. However, if I write
The resulting matrix is a 5*5. Therefore, the eigenvectors (calculated using eig) of covMat are a 5*5 matrix too, instead of a 144*5, like in the case of the socres obtained using pca. As far as I know I am doing this right, since in the documentation of cov it says that rows are observations and columns are random variables.
Can someone please tell me the difference between both methodologies?
How can I get a eigenvector of length 144?
Best regards .