Dear Mick, If all factors have two levels, then, with contr.sum (which you've apparently used here), the t-tests will be equivalent to "Type-III" F-tests. Alternatively, you can get either the "Type-II" or "Type-III" tests (most people prefer the former) from the Anova() function in the car package.
I hope this helps, John -------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox -------------------------------- > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] 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$Infected) > [,1] > I -1 > UI 1 > > contrasts(il4$Vaccinated) > [,1] > UV -1 > V 1 > > summary(lm(IL.4 ~ Infected * Vaccinated, il4)) > > Thanks > Mick > > -----Original Message----- > From: John Fox [mailto:[EMAIL PROTECTED] > Sent: 06 April 2005 12:52 > To: michael watson (IAH-C) > Cc: 'r-help'; [EMAIL PROTECTED] > 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: [EMAIL PROTECTED] > > [mailto:[EMAIL PROTECTED] On Behalf Of > > michael watson (IAH-C) > > Sent: Wednesday, April 06, 2005 4:31 AM > > To: [EMAIL PROTECTED] > > 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:[EMAIL PROTECTED] > > 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 > > > > ______________________________________________ > > [email protected] mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
