In addition to a screeplot, we can use the Kaiser method to determine the number of principal components. In this method, the selection criteria retain eigenvalues greater than 1. In this recipe, we demonstrate how to determine the number of principal components using the Kaiser method.
Ensure you have completed the previous recipe by generating a principal component object and saving it in variable eco.pca.
Perform the following steps to determine the number of principal components with the Kaiser method:
First, obtain the standard deviation from
eco.pca:> eco.pca$sdev [1] 2.2437007 1.3067890 0.9494543 0.7947934 0.6961356 0.6515563 [7] 0.5674359 0.5098891 0.4015613 0.2694394
Next, obtain the variance from
swiss.pca:> eco.pca$sdev ^ 2 [1] 5.0341927 1.7076975 0.9014634 0.6316965 0.4846048 0.4245256 [7] 0.3219835 0.2599869 0.1612515 0.0725976
Select the components with a variance above...
