[R-sig-ME] Specifying and fitting LME model with unstructured error correlation within subject

Thierry Onkelinx thierry@onkelinx @ending from inbo@be
Sun Dec 2 20:57:44 CET 2018

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
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel

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what the experiment died of. ~ Sir Ronald Aylmer Fisher
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The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of data.
~ John Tukey


Op vr 30 nov. 2018 om 18:20 schreef Kogan, Clark <clark.kogan using 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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