[R-sig-ME] Unidentifiable model in lmer

[Jake Westfall]

I believe this is a problem in lmerTest, not in lme4. I seem to recall running into a problem like this myself in the past, where lmerTest claimed that a model was not identifiable when I called summary() on it, even though I was pretty confident the model was fine and lmer() did not complain at all when fitting the model. (I don't think I ever figured out what was making it happen.)

Jake 

> To: r-sig-mixed-models at r-project.org
> From: taka.6765 at gmail.com
> Date: Thu, 17 Sep 2015 17:52:56 -0400
> Subject: [R-sig-ME] Unidentifiable model in lmer
> 
> Hi everyone,
> 
> I have been working on a linear mixed effect model by using lmer() 
> function, but I got an error saying that the fitted model is not 
> identifiable.
> 
> The data set includes the following variables:
> y = a numeric variable
> factorA = a 3-level categorical variable
> factorB = a 2-level categorical variable
> subjectID = subject id number. 2 measurements of y for each subject
> 
> R code and output are as follows:
>  > (result <- lmer(y ~ factorA + factorB + factorA*factorB + 
> (1|subjectID)))
> Linear mixed model fit by REML ['merModLmerTest']
> Formula: y ~ factorA + factorB + factorA * factorB + (1 | subjectID)
> REML criterion at convergence: 1928.966
> Random effects:
>   Groups    Name        Std.Dev.
>   subjectID (Intercept) 4711
>   Residual              7688
> Number of obs: 97, groups:  subjectID, 51
> Fixed Effects:
>        (Intercept)           factorA1           factorA2 factorB1 
> factorA1:factorB1  factorA2:factorB1
>              62411              -2700              -1124 
> -1037               1279               2482
>  > summary(result)
> Model is not identifiable...
> summary from lme4 is returned
> some computational error has occurred in lmerTest
> Linear mixed model fit by REML ['lmerMod']
> Formula: y ~ factorA + factorB + factorA * factorB + (1 | subjectID)
> 
> REML criterion at convergence: 1929
> 
> Scaled residuals:
>       Min       1Q   Median       3Q      Max
> -1.93423 -0.62611  0.01837  0.48887  2.73380
> 
> Random effects:
>   Groups    Name        Variance Std.Dev.
>   subjectID (Intercept) 22194074 4711
>   Residual              59108495 7688
> Number of obs: 97, groups:  subjectID, 51
> 
> Fixed effects:
>                    Estimate Std. Error t value
> (Intercept)          62411       2065  30.227
> factorA1             -2700       3238  -0.834
> factorA2             -1124       3008  -0.374
> factorB1             -1037       2512  -0.413
> factorA1:factorB1     1279       4013   0.319
> factorA2:factorB1     2482       3642   0.681
> 
> Correlation of Fixed Effects:
>              (Intr) fctrA1 fctrA2 fctrB1 fA1:B1
> factorA1    -0.638
> factorA2    -0.687  0.438
> factorB1    -0.608  0.388  0.418
> fctrA1:fcB1  0.381 -0.601 -0.262 -0.626
> fctrA2:fcB1  0.420 -0.268 -0.606 -0.690  0.432
>  >
> 
> Could anyone give me some idea of why this unidentifiability problem 
> happens and how to fix it?
> Any help would be appreciated.
> 
> Best regards,
> Takahiro
> 
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