[R] [OT] correspondence analysis w/ non-mutually-exclusive categories

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From: Andrew Perrin (aperrin@socrates.berkeley.edu)
Date: Fri 02 Mar 2001 - 07:50:08 EST 

Message-ID: <Pine.LNX.4.21.0103011643300.1325-100000@nujoma.perrins>

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@socrates.berkeley.edu - aperrin@igc.apc.org

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