You're at the mercy of the various contr.XXX functions. They may or may not set
the colnames on the matrices that they generate.
The rationales for (not) setting them is not perfectly transparent, but you
obviously cannot use level names on contr.poly, so it uses .L, .Q, etc.
In MASS, contr.sdif is careful about labeling the columns with the levels that
are being diff'ed.
For contr.treatment, there is a straightforward connection to 0/1 dummy
variables, so level names there are natural.
One could use levels in contr.sum and contr.helmert, but it might confuse users
that comparisons are with the average of all levels or preceding levels. (It
can be quite confusing when coding is +1 for male and -1 for female, so that
the gender difference is twice the coefficient.)
-pd
> On 14 Jun 2024, at 08:12 , Christophe Dutang <duta...@gmail.com> wrote:
>
> Dear list,
>
> Changing the default contrasts used in glm() makes me aware how
> model.matrix() set column names.
>
> With default contrasts, model.matrix() use the level values to name the
> columns. However with other contrasts, model.matrix() use the level indexes.
> In the documentation, I don’t see anything in the documentation related to
> this ? It does not seem natural to have such a behavior?
>
> Any comment is welcome.
>
> An example is below.
>
> Kind regards, Christophe
>
>
> #example from ?glm
> counts <- c(18,17,15,20,10,20,25,13,12)
> outcome <- paste0("O", gl(3,1,9))
> treatment <- paste0("T", gl(3,3))
>
> X3 <- model.matrix(counts ~ outcome + treatment)
> X4 <- model.matrix(counts ~ outcome + treatment, contrasts =
> list("outcome"="contr.sum"))
> X5 <- model.matrix(counts ~ outcome + treatment, contrasts =
> list("outcome"="contr.helmert"))
>
> #check with original factor
> cbind.data.frame(X3, outcome)
> cbind.data.frame(X4, outcome)
> cbind.data.frame(X5, outcome)
>
> #same issue with glm
> glm.D93 <- glm(counts ~ outcome + treatment, family = poisson())
> glm.D94 <- glm(counts ~ outcome + treatment, family = poisson(), contrasts =
> list("outcome"="contr.sum"))
> glm.D95 <- glm(counts ~ outcome + treatment, family = poisson(), contrasts =
> list("outcome"="contr.helmert"))
>
> coef(glm.D93)
> coef(glm.D94)
> coef(glm.D95)
>
> #check linear predictor
> cbind(X3 %*% coef(glm.D93), predict(glm.D93))
> cbind(X4 %*% coef(glm.D94), predict(glm.D94))
>
> -------------------------------------------------
> Christophe DUTANG
> LJK, Ensimag, Grenoble INP, UGA, France
> ILB research fellow
> Web: http://dutangc.free.fr
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: pd....@cbs.dk Priv: pda...@gmail.com
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