lib/statsample/factor/pca.rb in statsample-1.4.3 vs lib/statsample/factor/pca.rb in statsample-1.5.0

@@ -11,11 +11,11 @@
   # eigenvector could have negative values!
   # For Principal Axis Analysis, use Statsample::Factor::PrincipalAxis
   # 
   # == Usage:
   #   require 'statsample'
-  #   a=[2.5, 0.5, 2.2, 1.9, 3.1, 2.3, 2.0, 1.0, 1.5, 1.1].to_scale
-  #   b=[2.4,0.7,2.9,2.2,3.0,2.7,1.6,1.1,1.6,0.9].to_scale
+  #   a=[2.5, 0.5, 2.2, 1.9, 3.1, 2.3, 2.0, 1.0, 1.5, 1.1].to_numeric
+  #   b=[2.4,0.7,2.9,2.2,3.0,2.7,1.6,1.1,1.6,0.9].to_numeric
   #   ds={'a'=>a,'b'=>b}.to_dataset
   #   cor_matrix=Statsample::Bivariate.correlation_matrix(ds)
   #   pca=Statsample::Factor::PCA.new(cor_matrix)
   #   pca.m
   #   => 1