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how to tell if its better to standardize your data matrix first when you do principal

masterinex 
Hi guys , 

Im trying to do principal component analysis in R . There is 2 ways of doing
it , I believe. 
One is doing  principal component analysis right away the other way is 
standardizing the matrix first  using s = scale(m)and then apply principal
component analysis.   
How  do I tell what result is better ? What values in particular should i
look at . I already managed to find the eigenvalues and eigenvectors , the
proportion of  variance for each eigenvector using both methods.



I noticed that the proportion of the variance for the first  pca without
standardizing had a larger  value . Is there a meaning to it ? Isnt this
always the case?
 At last , if I am  supposed to predict a variable ie weight should I drop
the variable ie weight from my data matrix when I do principal component
analysis ?

Thread (10 messages)

masterinex how to tell if its better to standardize your data matrix first when you do principal Nov 21 Uwe Ligges how to tell if its better to standardize your data matrix first when you do principal Nov 22 masterinex how to tell if its better to standardize your data matrix first when you do principal Nov 22 Hadley Wickham how to tell if its better to standardize your data matrix first when you do principal Nov 22 masterinex how to tell if its better to standardize your data matrix first when you do principal Nov 22 masterinex how to tell if its better to standardize your data matrix first when you do principal Nov 22 Uwe Ligges how to tell if its better to standardize your data matrix first when you do principal Nov 23 Uwe Ligges how to tell if its better to standardize your data matrix first when you do principal Nov 23 Michael Kubovy how to tell if its better to standardize your data matrix first when you do principal Nov 23 masterinex how to tell if its better to standardize your data matrix first when you do principal Nov 23