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How to related PCA output to the original data?

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Yaser Khojah
Yaser Khojah on 18 Apr 2019
Commented: Adam on 18 Sep 2019
I'm new to PCA and I would like to learn the outcome of pca function. I have read the document and checked others works but I'm a bit confused on how to related the results (wcoeff, latent, explained) to the original data. For example, I'm using the example from the document as below. I understand the (wcoeff) presents the eigenvector vectors. the (latent) presents the eigenvalues. the (explained) is the percentage of the total variance explained by each principal component. NOW, how are all these information are related to the main data which is the ingredients here? how do I know from looking at the results in (explained) that the 55 % is related to which variables or columns in the ingredients matrix?
load hald
lained] = pca(ingredients,'VariableWeights','variance')
wcoeff = 4×4
-2.7998 2.9940 -3.9736 1.4180
-8.7743 -6.4411 4.8927 9.9863
2.5240 -3.8749 -4.0845 1.7196
9.1714 7.5529 3.2710 11.3273
latent = 4×1
explained = 4×1


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As Has
As Has on 18 Sep 2019
i don't understand variable 2 has 11.3 it is greater than variable 5
plz explain this...thank you
Adam on 18 Sep 2019
The variables are in the rows of the coeff matrix. The columns are the principal components eigenvectors.

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