R help - Mar 2001 - [OT] correspondence analysis w/ non-mutually-exclusive ca tegories

Andy,
Take a look at Greenacre, Theory and Applications of Correspondence
Analysis.  He has many example of dealing with all sorts of data.
Basically, the technique is relevant for 2-way tables -- MCA is an
extenstion.  It is not clear in your example whether CA is really
appropriate -- you want to make an  observation (if at all possible) fall in
one cell, treating the others layers as 'supplementary' points --
without
necessarily contributing to the 'factors.'  So the first step is to
cross
classify your data into a table and then apply CA.


Marwan Khawaja
Fafo Institute 
Borggt. 2B, P.O.Box 2947 T?yen
N-0608 Oslo, Norway
> -----Original Message-----
> From:	Andrew Perrin [SMTP:aperrin at socrates.berkeley.edu]
> Sent:	1. mars 2001 21:50
> To:	r-help at r-project.org
> Subject:	[R] [OT] correspondence analysis w/ non-mutually-exclusive
> categories
> 
> Greetings, again. This is not strictly an R question, so please feel free
> to ignore it if you like. 
> 
> My question is about the substance of correspondence
> analysis. Specifically, is it appropriate to use ca on a matrix of values
> such that the columns and/or rows are not mutually exclusive?  To be more
> detailed:
> 
> - The standard use of ca is illustrated in the example of corresp() (from
> MASS):
> 
> data(caith)
> library(mva)
> corresp(caith)
> biplot(corresp(caith, nf=2))
> 
> > caith
>        fair red medium dark black
> blue    326  38    241  110     3
> light   688 116    584  188     4
> medium  343  84    909  412    26
> dark     98  48    403  681    85
> 
> in this table, presumably, an observation can fall in only one
> cell: red/light, say, or dark/fair.  
> 
> - However, my data are different, in that a single observation can
> (theoretically) fall in two or more cells. Consider:
> 
> 	voted98	voted00	donated	protested no_partic
> male
> female
> 
> a given observation might fall, for example, in male/voted98 and
> male/voted00.  What are the implications of this?
> 
> - I am aware of the multiple correspondence technique, which I believe
> answers (some of) this issue. However I have a different problem with
> it: I have so many observations (ca. 5700) that the plot becomes
> unreadable. That's because each *observation* is plotted in mca,
whereas
> each unique profile is what's plotted in ca.
> 
> Any advice will be met with tremendous gratitude :)
> 
> Andy Perrin
> 
> ----------------------------------------------------------------------
> Andrew J Perrin - Ph.D. Candidate, UC Berkeley, Dept. of Sociology  
> Chapel Hill, North Carolina, USA - http://demog.berkeley.edu/~aperrin
>         aperrin at socrates.berkeley.edu - aperrin at igc.apc.org
> 
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