[R] Measure of Redundancy In Variables

Spencer Graves spencer.graves at pdf.com
Thu Mar 20 19:30:01 CET 2003

Have you considered "factanal" in library "mva"?  How do the 
"uniquenesses" in the "factanal" object relate to what you want?  From 
what I read of your question, it sounds like they estimate exactly what 
you want.

Hope this helps.
Spencer Graves

Rishabh Gupta wrote:
> Hi all,
>   I have a question which I guess is more of a general stats question than a specific R quetions.
> I have a data set that contains a large number of numerical variables (in the hundreds). What I
> would like to do is quantify the redundancy in those variables. Let me explain what I mean by
> that.
> If I use Principle Component Analysis (PCA) to reduce the amount of variables, the process
> measures the relationship between the different variables and reorganises it so that each variable
> provides unique information and removes any redundancy between different variables. What I would
> like to do is a kind of measure between the data before PCA and after PCA. For example, if there
> is no redundancy, i.e. all of the pre-PCA variables provide unique information, the redundancy
> rate would be 100%. On the other hand if all the pre-PCA variables provide the same information
> than the redundancy rate would be 1%.
> Could anyone tell me if there is a method of measuring this redundancy rate or something similar
> in R.
> If somebody could help me with this issue it would be greatly appreciated. Many Thanks
> Rishabh
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