[R-sig-ME] fixed-effect model matrix is rank deficient so dropping 1 column / coefficient

[IAGO GINÉ VÁZQUEZ]

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 using inbo.be>
Enviat el: dimecres, 3 de març de 2021 13:30
Per a: IAGO GINÉ VÁZQUEZ <i.gine using pssjd.org>
A/c: r-sig-mixed-models using r-project.org <r-sig-mixed-models using 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 using inbo.be<mailto:thierry.onkelinx using inbo.be>
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be<http://www.inbo.be>

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Op wo 3 mrt. 2021 om 13:11 schreef IAGO GINÉ VÁZQUEZ <i.gine using pssjd.org<mailto:i.gine using 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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