Thanks for your reply.
It might good to document the naming convention in ?contrasts. It is hard to
understand .L for linear, .Q for quadratic, .C for cubic and ^n for other
degrees.
For contr.sum, we could have used .Sum<level1>, .Sum<level2>…
Maybe the examples ?model.matrix should use names in dd objects so that we
observe when names are dropped.
Kind regards, Christophe
> Le 14 juin 2024 à 11:45, peter dalgaard <pda...@gmail.com> a écrit :
>
> 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
>>
>> ______________________________________________
>> R-help@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.
>
> --
> 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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