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
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