The parameter is different because the model without intercept assumes that effect of f1 is independent on the effect of f2. So you force f1b:f2ll to be 0.
The interpretation is the same. The fit is conditional on the model (interaction or no interaction). ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey 2015-04-26 17:40 GMT+02:00 Mario José Marques-Azevedo <[email protected]> : > Dear Thierry, > > That is the problem. I read that interpretation is the same, but the > Intercept value of summary is different: > > The mean of level "a" of f1 and level "I" of f2 (first level of each > factor) is 0.7127851. > > When I run model with interaction term: > > summary.lm(aov(y~f1*f2,data=dt)) > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 0.7128 0.2884 2.471 0.0484 * > f1b 1.0522 0.4560 2.307 0.0605 . > f2II -0.6787 0.4560 -1.488 0.1872 > f1b:f2II -1.1741 0.6449 -1.821 0.1185 > > I check that Intercept is mean of level "a" of f1 and level "I" of f2. > > But when I run the model without interaction term, the Intercept value is > different: > > summary.lm(aov(y~f1+f2,data=dt)) > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 0.9476 0.2976 3.185 0.0154 * > f1b 0.4651 0.3720 1.251 0.2513 > f2II -1.2658 0.3720 -3.403 0.0114 * > > I do not know what is Intercept value in this case. I expected that it is > mean of level "a" of f1 and level "I" of f2, but not. > > Best regards, > > Mario > > > On 26 April 2015 at 12:30, Thierry Onkelinx <[email protected]> > wrote: > > > Dear Mario, > > > > The interpretation is the same: the average at the reference situation > > which is the group that has f1 == "f1 level1" and f2 == "f2 level1". > > > > Best regards, > > > > ir. Thierry Onkelinx > > Instituut voor natuur- en bosonderzoek / Research Institute for Nature > and > > Forest > > team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance > > Kliniekstraat 25 > > 1070 Anderlecht > > Belgium > > > > To call in the statistician after the experiment is done may be no more > > than asking him to perform a post-mortem examination: he may be able to > say > > what the experiment died of. ~ Sir Ronald Aylmer Fisher > > The plural of anecdote is not data. ~ Roger Brinner > > The combination of some data and an aching desire for an answer does not > > ensure that a reasonable answer can be extracted from a given body of > data. > > ~ John Tukey > > > > 2015-04-26 17:12 GMT+02:00 Mario José Marques-Azevedo < > > [email protected]>: > > > >> Hi all, > >> > >> I am doing anova multi factor and I found different Intercept when model > >> has interaction term. > >> > >> I have the follow data: > >> > >> set.seed(42) > >> dt <- data.frame(f1=c(rep("a",5),rep("b",5)), > >> f2=rep(c("I","II"),5), > >> y=rnorm(10)) > >> > >> When I run > >> > >> summary.lm(aov(y ~ f1 * f2, data = dt)) > >> > >> The Intercept term is the mean of first level of f1 and f2. I can > confirm > >> that with: > >> > >> tapply(dt$y, list(dt$f1, dt$f2), mean) > >> > >> I know that others terms are difference of levels with Intercept. > >> > >> But I do not know what is Intercept when the model do not have > interaction > >> term: > >> > >> summary.lm(aov(y ~f1 + f2, data = dt)) > >> > >> I know that I can create a specific contrast table, by I would like > >> understand the default R output. > >> > >> I read contrast sub-chapter on Crawley 2012 (The R book) and in his > >> example > >> the Intercept is different when model has or not interaction term, but > he > >> explain that Intercept is mean of first level of the factors. > >> > >> Best regards, > >> > >> Mario > >> > >> ............................................................. > >> Mario José Marques-Azevedo > >> Ph.D. Candidate in Ecology > >> Dept. Plant Biology, Institute of Biology > >> University of Campinas - UNICAMP > >> Campinas, São Paulo, Brazil > >> > >> [[alternative HTML version deleted]] > >> > >> ______________________________________________ > >> [email protected] mailing list -- To UNSUBSCRIBE and more, see > >> https://stat.ethz.ch/mailman/listinfo/r-help > >> PLEASE do read the posting guide > >> http://www.R-project.org/posting-guide.html > >> and provide commented, minimal, self-contained, reproducible code. > > > > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
