The problem with applying prcomp to binary data is that it's not
clear what problem you are solving.
The standard principal components and factor analysis models
assume that the observations are linear combinations of unobserved
"common" factors (shared variability), normally distributed, plus normal
noise, independent between observations and variables. Those
assumptions are clearly violated for binary data.
RSiteSearch("PCA for binary data") produced references to 'ade4'
and 'FactoMineR'. Have you considered these? I have not used them, but
FactoMineR included functions for 'Multiple Factor Analysis for Mixed
[quantitative and qualitative] Data'
Hope this helps.
Spencer Graves
Josh Gilbert wrote:
> I don't understand, what's wrong with using prcomp in this situation?
>
> On Sunday 10 June 2007 12:50 pm, Ranga Chandra Gudivada wrote:
>
>> Hi,
>>
>> I was wondering whether there is any package implementing Principal
>> Component Analysis for Binary data
>>
>> Thanks chandra
>>
>>
>> ---------------------------------
>>
>>
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