[R] 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 ?
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