Specifying and fitting LME model with unstructured error correlation within subject

Dear Kogan,

Add (1|id) as random effect. This will induce a correlation among the
observations from the same individual.

Best regards,

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 vr 30 nov. 2018 om 18:20 schreef Kogan, Clark <clark.kogan at wsu.edu>:

I have some data where a number of individuals have taken a few different
subtests and there is 1 response per individual for each subtest. I am
fitting the following model using lmer:

mod <- lmer(score ~ faculty + gender + subtest + gender:subtest +
faculty:gender + faculty:subtest+ (subtest|id), data = score)

When fitting this model, I get the error:
Error: number of observations (=219) <= number of random effects (=219)
for term (subtest | id); the random-effects parameters and the residual
variance (or scale parameter) are probably unidentifiable

The error makes sense to me - as there is only one data point for every
subtest*id, and so we cannot differentiate the random effects from the
residuals. What I would like to be able to do is specify that the

residuals

have an unstructured correlation matrix within individuals to account for
the fact that an individual will likely have some correlation between

their

subtest scores.

Is there a way to do this in lmer or a similar package so that I can

still

get Kenwood Rodgers or Satterthwaite corrected tests of effects (e.g.,

with

pbkrtest or lmerTest).

Thanks,
Clark


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