[R] Contrast anova multi factor

[Thierry Onkelinx]

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 <mariojmaaz op gmail.com>
:

> ​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 <thierry.onkelinx op inbo.be>
> 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 <
> > mariojmaaz op gmail.com>:
> >
> >> 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]]
> >>
> >> ______________________________________________
> >> R-help op r-project.org 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]]
>
> ______________________________________________
> R-help op r-project.org 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]]