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


///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
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

///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


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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Thierry,

I believe this will induce a compound symmetric covariance structure rather
than an unstructured covariance structure. I would like to allow for unique
correlations between different subtests.

Thanks,
Clark


On Sun, Dec 2, 2018 at 11:58 AM Thierry Onkelinx via R-sig-mixed-models <
r-sig-mixed-models at r-project.org> wrote:

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


///////////////////////////////////////////////////////////////////////////////////////////
To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to say
what the experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
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

///////////////////////////////////////////////////////////////////////////////////////////

<https://www.inbo.be>


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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