fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

Dear Thierry,

Thank you for the ask. I send you the data attached. The model I am using
is

lmer(outcome ~ YLD0 + YLD1 + YLD2 + YLD3 + YLD4 + SEV1 + SEV2 + SEV3 +
SEV4 + SEV5 + SEV6 + x0*x1 + x2 + x0*x3 + year + (1|id), data=data2Sh).

The factor variable is x0. The dropped coefficient is x0==2 : x3

Let me know any other information I can give.

Thank you!


*Iago *
------------------------------
*De:* Thierry Onkelinx <thierry.onkelinx at inbo.be>
*Enviat el:* dimecres, 3 de mar? de 2021 13:30
*Per a:* IAGO GIN? V?ZQUEZ <i.gine at pssjd.org>
*A/c:* r-sig-mixed-models at r-project.org <r-sig-mixed-models at r-project.org>
*Tema:* Re: [R-sig-ME] fixed-effect model matrix is rank deficient so
dropping 1 column / coefficient

Dear Iago,

We only have your description of the data. It would be easier for us to
help you if you provide a small dataset that illustrates the structure in
your data and how you use the data in your model.

Best regards,

Thierry

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND
FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be


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Op wo 3 mrt. 2021 om 13:11 schreef IAGO GIN? V?ZQUEZ <i.gine at pssjd.org>:

Dear all,

I have 3 related questions, probably already answered, but which I cannot
find:

When computing a model with lmer I get the message

fixed-effect model matrix is rank deficient so dropping 1 column /
coefficient

Then, my questions are, first, how can I see/compute/get the rank
deficient fixed-effect model matrix, second how is that matrix computed,
and third (these actually are 2 questions), if my model is yet valid (is
it?) how can the dropped fixed effect explained in the results of a paper.

In the example in ?fixef
fm2 <- lmer(Reaction ~ Days + Days2 + (1|Subject),
            data=transform(sleepstudy,Days2=Days))
fixef(fm2,add.dropped=TRUE)

the problem happens because 2 independent variables are equal, but in my
model the numeric independent variables are not so highly correlated. In
fact the problem happens with the interaction between a factor and a
numeric variable, since it is one of the categories of the factor
interacting with the numeric, which is dropped.

Thank you and stay safe!


Iago


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