Determining the number of principal components using the Kaiser method
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.
Getting ready
Ensure you have completed the previous recipe by generating a principal component object and saving it in variable eco.pca.
How to do it…
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...
