# Eigenvalues from prcomp

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?

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