It's sorta-kinda-obliquely-partially documented in the examples:
zapsmall(cP <- contr.poly(3)) # Linear and Quadratic
output:
.L .Q
[1,] -0.7071068 0.4082483
[2,] 0.0000000 -0.8164966
[3,] 0.7071068 0.4082483
FWIW the faux package provides better-named alternatives.
On 2024-06-17 4:25 p.m., Christophe Dutang wrote:
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
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--
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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--
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(Acting) Graduate chair, Mathematics & Statistics
> E-mail is sent at my convenience; I don't expect replies outside of
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