-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of
michael watson (IAH-C)
Sent: Wednesday, April 06, 2005 7:18 AM
To: John Fox
Cc: r-help
Subject: RE: [R] Help with three-way anova
Hi John
Thanks for your help, that was a very clear answer. It looks
as though, due to my design, the best way forward is:
contrasts(il4$Vaccinated)
summary(lm(IL.4 ~ Infected * Vaccinated, il4))
Thanks
Mick
-----Original Message-----
From: John Fox [mailto:jfox at mcmaster.ca]
Sent: 06 April 2005 12:52
To: michael watson (IAH-C)
Cc: 'r-help'; f.calboli at imperial.ac.uk
Subject: RE: [R] Help with three-way anova
Dear Mick,
For a three-way ANOVA, the difference between aov() and lm() is mostly
in the print and summary methods -- aov() calls lm() but in
its summary
prints an ANOVA table rather than coefficient estimates, etc. You can
get the same ANOVA table from the object returned by lm via
the anova()
function. The problem, however, is that for unbalanced data you'll get
sequential sums of squares which likely don't test hypotheses of
interest to you.
If you didn't explicitly set the contrast coding, then the out-of-box
default in R [options("contrasts")] is to use treatment.contr(), which
produces dummy-coded (0/1) contrasts. In this case, the "intercept"
represents the fitted value when all of the factors are at their
baseline levels, and it's probably entirely uninteresting to test
whether it is 0.
More generally, however, it seems unreasonable to try to learn how to
fit and interpret linear models in R from the help files. There's a
brief treatment in the Introduction to R manual that's
distributed with
R, and many other more detailed treatments -- see
http://www.r-project.org/other-docs.html.
Regards,
John
--------------------------------
John Fox
Department of Sociology
McMaster University
Hamilton, Ontario
Canada L8S 4M4
905-525-9140x23604
http://socserv.mcmaster.ca/jfox
--------------------------------
-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of
michael watson (IAH-C)
Sent: Wednesday, April 06, 2005 4:31 AM
To: f.calboli at imperial.ac.uk
Cc: r-help
Subject: RE: [R] Help with three-way anova
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 at 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
not identical. If I do anova(My.aov) and anova(My.lm) I get
results. I guess that's to be expected though.
Regarding the results of summary(My.lm), basically
and Vaccinated are all significant at p<=0.05. I presume the
signifcance of the Intercept is that it is significantly
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