Although John Fox naturally mentions his Anova function, I would like to
point out that drop1() (and MASS::dropterm) also does the tests of Type-II
ANOVA of which John says 'more tediously do these tests directly'.
It seems a lot easier to teach newcomers about drop1() than to introduce
the SAS terminology and then say (to quote ?Anova)
'the definitions used here do not correspond precisely to those
employed by SAS'
(I would welcome a description of the precise differences on the Anova
help page.)
On Fri, 13 Jun 2008, John Fox wrote:
Dear Kimmo,
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On
Behalf Of K. Elo
Sent: June-13-08 1:43 AM
To: r-help@r-project.org
Subject: Re: [R] MCA in R
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.
That's because anova() calculates sequential ("type-I") sums of squares; if
you use the Anova() function in the car package, for example, you'll get
so-called type-II sums of squares -- for each factor after the others. You
could also more tediously do these tests directly using the anova()
function, by contrasting alternative models: the full model and the model
deleting each factor in turn.
A colleague of mine has run anova and MCA in SPSS and the results differ
significantly.
Yes, see above.
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.
Aren't the eta values just the square-roots of the R^2's from the individual
one-way ANOVAs? I don't remember how the betas are defined, but do recall
that they are a peculiar attempt to define standardized partial regression
coefficients for factors that combine all of the levels.
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 :)
If you tell me what you did, ideally including an example that I can
reproduce, I can probably tell you what's wrong.
Regards,
John
Kind regards,
Kimmo Elo
--
University of Turku, Finland
Dep. of political science
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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