masterinex wrote:
Hi Hadley ,
I really apreciate the suggestions you gave, It was helpful , but I still
didnt quite get it all. and I really want to do a good job , so any
comments would sure come helpful, please understand me .
Well, we try to understand you, but we do not either. I think you really
nedc to consult some statistics textbook on PCA if my answer was not
sufficient. Given your questions, I doubt you understand what PCA does
and how it works. It does not predict anything.
Uwe Ligges
hadley wrote:
You've asked the same question on stackoverflow.com and received the
same answer. This is rude because it duplicates effort. If you
urgently need a response to a question, perhaps you should consider
paying for it.
Hadley
On Sun, Nov 22, 2009 at 12:04 PM, masterinex <xevilgan...@hotmail.com>
wrote:
so under which cases is it better to standardize the data matrix first
?
also is PCA generally used to predict the response variable , should I
keep that variable in my data matrix ?
Uwe Ligges-3 wrote:
masterinex wrote:
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.
Generally, it is better to standardize. But in some cases, e.g. for the
same units in your variables indicating also the importance, it might
make sense not to do so.
You should think about the analysis, you cannot know which result is
`better' unless you know an interpretation.
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 ?
This sounds a bit like homework. If that is the case, please ask your
teacher rather than this list.
Anyway, it does not make sense to predict weight using a linear
combination (principle component) that contains weight, does it?
Uwe Ligges
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