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

[masterinex]

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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> 

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