I'm running a PCA using prcomp in R and I get a table like this from the summary function:
Importance of components: PC1 PC2 PC3 PC4 Standard deviation 227.5998 86.2614 6.76700 3.29498 Proportion of Variance 0.8736 0.1255 0.00077 0.00018 Cumulative Proportion 0.8736 0.9990 0.99982 1.00000
Which gives me eigenvalues of:
PC1 PC2 PC3 PC4 51801.68 7441.03 45.79 10.86
My understanding was that any variable with eigenvalues greater than 1 are considered important but these seem ridiculously high. And I wouldn't think a principal component with a proportion of variance of 0.00018 to be too important. All my data seems fine.