[R] MCA in R

[K. Elo]

Dear John,

thanks for Your quick reply.

> John Fox wrote:
> Dear Kimmo,
> 
> MCA is a rather old name (introduced, I think, in the 1960s by
> Songuist and Morgan in the OSIRIS package) for a linear model
> consisting entirely of factors and with only additive effects --
> i.e., an ANOVA model will no interactions.

It is true, that MCA is an old name, but the technique itself is still 
robust, I think. The problem I am facing is that I have a research 
project where I try to find out which factors affect measured knowledge 
of a specific issue. As predictors I have formal education, interest, 
gender and consumption of different medias (TV, newspapers etc.). Now, 
these are correlated predictors and running e.g. a simple anova 
(anova(lm(...)) as You suggested) won't - if I have understood correctly 
- consider the problem of correlated predictors. MCA would do this.

A colleague of mine has run anova and MCA in SPSS and the results differ 
significantly. Because I am more familiar with R, I just hoped that this 
marvelous statistical package could handle MCA, too :)

> Typically, the results of
> an MCA are reported using "adjusted means." You could compute these
> manually, or via the effects package.

Well, I am interested in the eta and beta values, too. I have tried to 
use the effects package but my attempts with all.effects resulted in 
errors. I have to figure out what's going wrong here :)

Kind regards,
Kimmo Elo

--
University of Turku, Finland
Dep. of political science