RE: [R] Help with three-way anova

From: michael watson (IAH-C) <michael.watson_at_bbsrc.ac.uk>
Date: Wed 06 Apr 2005 - 19:30:53 EST


OK, now I am lost.

I went from using aov(), which I fully understand, to lm() which I probably don't. I didn't specify a contrasts matrix in my call to lm()....

Basically I want to find out if Infected/Uninfected affects the level of IL.4, and if Vaccinated/Unvaccinated affects the level of IL.4, obviously trying to separate the effects of Infection from the effects of Vaccination.

The documentation for specifying contrasts to lm() is a little convoluted, sending me to the help file for model.matrix.default, and the help there doesn't really give me much to go on when trying to figure out what contrasts matrix I need to use...

Many thanks for your help

Mick

-----Original Message-----
From: Federico Calboli [mailto:f.calboli@imperial.ac.uk] Sent: 06 April 2005 10:15
To: michael watson (IAH-C)
Cc: r-help
Subject: RE: [R] Help with three-way anova

On Wed, 2005-04-06 at 09:11 +0100, michael watson (IAH-C) wrote:
> OK, so I tried using lm() instead of aov() and they give similar
> results:
>
> My.aov <- aov(IL.4 ~ Infected + Vaccinated + Lesions, data)
> My.lm <- lm(IL.4 ~ Infected + Vaccinated + Lesions, data)

Incidentally, if you want interaction terms you need

lm(IL.4 ~ Infected * Vaccinated * Lesions, data)

for all the possible interactions in the model (BUT you need enough degrees of freedom from the start to be able to do this).
>
> If I do summary(My.lm) and summary(My.aov), I get similar results, but

> not identical. If I do anova(My.aov) and anova(My.lm) I get identical
> results. I guess that's to be expected though.
>
> Regarding the results of summary(My.lm), basically Intercept, Infected

> and Vaccinated are all significant at p<=0.05. I presume the
> signifcance of the Intercept is that it is significantly different to
> zero? How do I interpret that?

I guess it's all due to the contrast matrix you used. Check with contrasts() the term(s) in the datafile you use as independent variables, and change the contrast matrix as you see fit.

HTH, F

-- 
Federico C. F. Calboli
Department of Epidemiology and Public Health
Imperial College, St Mary's Campus
Norfolk Place, London W2 1PG

Tel  +44 (0)20 7594 1602     Fax (+44) 020 7594 3193

f.calboli [.a.t] imperial.ac.uk
f.calboli [.a.t] gmail.com

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Received on Wed Apr 06 19:34:57 2005

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