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I used prcomp to calculate the follow PCA values:

                PC1        PC2        PC3         PC4          PC5         PC6
logPower  0.6789041 -0.3370631  0.1337237  0.63152740 -0.092702676  0.01323106
logSpan   0.1475060  0.4778834  0.2150124  0.06127707 -0.048909835 -0.83515987
logLength 0.2128740  0.2307281  0.1568771 -0.27071156 -0.863486982  0.24071613
logWeight 0.5880074  0.2578687  0.2481639 -0.48182127  0.480207328  0.25182325
logSpeed  0.2291724 -0.6301924 -0.2741496 -0.53235281 -0.112767421 -0.42315895
logRange  0.2715571  0.3756882 -0.8800761  0.09264058 -0.009418365  0.04370613

The eigenvalues are located across the diagonal of this matrix, right? So the largest eigenvalue would be 0.6789041, second largest 0.4778834, and so on?

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No, those are just eigenvectors. You need to save the output of prcomp into a variable and then look at the sdev component of that variable. Squaring the sdev component gets you the eigenvalues.

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  • $\begingroup$ (+1) or you can use princomp(data) and square the values given in the output to obtain the eigenvalues. $\endgroup$
    – Stochastic
    Commented Aug 11, 2020 at 9:30

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