[R-sig-ME] How to interpret verbose output

[Stuart Luppescu]

Hello, I'm trying to do a variance decomposition of teacher performance
ratings. Each teacher is observed four times and rated on 9 components.
The model looks like this:

 lme8 <- lmer(rating ~
              (1|tid.f) + (1|obsorder.f) + (1|comp.f) +
               (1|tid.f:comp.f) +  (1|obsorder.f:comp.f) + (1|
tid.f:obsorder.f) ,
               data=ratings, REML=FALSE, verbose=2)

where tid.f is the teacher identifier, comp.f the component identifier,
and obsorder.f the observation number. 

This works fine for the whole dataset, but I want to do it separately by
decile based on the average rating for each teacher, so I added this: ,
subset=ratings$bins==quantile
where the bins are {1, ..., 10} indicating the decile, and quantile is a
loop index used like this: for(quantile in 1:10) {}

It works fine for the lowest decile, but fails for every decile after
that. I get 0.00 for the tid.f variance component, which is the one I'm
really interested in. I have no idea why. I checked the distribution of
average ratings by decile and it all looks unremarkable. I think there
may be a clue in the iteration details as shown by the verbose output
but I don't know how to interpret it. Here it is for decile 1:

npt = 8 , n =  6 
rhobeg =  0.2 , rhoend =  2e-07 
   0.020:  16:      18359.8;0.454554 0.735854 0.376302 0.705437 0.787591
0.802885 
  0.0020:  32:      18346.2;0.454004 0.699893 0.407403 0.574987 0.702683
0.880645 
 0.00020:  77:      18263.7;0.432231 0.697895 0.398860 0.0461275
0.326475  1.55039 
 2.0e-05: 292:      18252.6;0.432051 0.700841 0.394275 0.0472994
0.315551 0.130071 
 2.0e-06: 339:      18252.6;0.432029 0.700839 0.394515 0.0472285
0.314849 0.129962 
 2.0e-07: 364:      18252.6;0.432028 0.700822 0.394548 0.0472181
0.314777 0.129904 
At return
395:     18252.578: 0.432030 0.700822 0.394554 0.0472204 0.314755
0.129913
>   print(summary(lme8))
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: rating ~ (1 | tid.f) + (1 | obsorder.f) + (1 | comp.f) + (1 |  
    tid.f:comp.f) + (1 | obsorder.f:comp.f) + (1 | tid.f:obsorder.f)
   Data: ratings
 Subset: ratings$bins == quantile

     AIC      BIC   logLik deviance df.resid 
 18268.6  18328.1  -9126.3  18252.6    12622 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.2200 -0.5592 -0.0306  0.5711  4.4942 

Random effects:
 Groups            Name        Variance Std.Dev.
 tid.f:comp.f      (Intercept) 0.033067 0.18184 
 tid.f:obsorder.f  (Intercept) 0.087013 0.29498 
 tid.f             (Intercept) 0.027579 0.16607 
 obsorder.f:comp.f (Intercept) 0.000395 0.01988 
 comp.f            (Intercept) 0.017551 0.13248 
 obsorder.f        (Intercept) 0.002990 0.05468 
 Residual                      0.177161 0.42090 
Number of obs: 12630, groups: tid.f:comp.f, 3159; tid.f:obsorder.f,
1404; tid.f, 351; obsorder.f:comp.f, 45; comp.f, 9; obsorder.f, 5

Fixed effects:
            Estimate Std. Error t value
(Intercept)  2.01657    0.05355   37.65

And here for decile 5:

npt = 8 , n =  6 
rhobeg =  0.2 , rhoend =  2e-07 
   0.020:  18:      15847.4;0.261469 0.514852 0.178718 0.0962381
0.152470 0.160530 
  0.0020:  35:      15749.5;0.356245 0.532056  0.00000 0.0961818
0.219586 0.166497 
 0.00020:  90:      15742.1;0.349036 0.516678  0.00000 0.0755764
0.330633 0.248861 
 2.0e-05: 107:      15742.1;0.348612 0.515454  0.00000 0.0766747
0.332641 0.251693 
 2.0e-06: 250:      15742.1;0.348335 0.515211  0.00000 0.0765898
0.330456 0.260112 
 2.0e-07: 273:      15742.1;0.348339 0.515207  0.00000 0.0765856
0.330510 0.260162 
At return
281:     15742.076: 0.348339 0.515207 1.97296e-07 0.0765858 0.330510
0.260162
>   print(summary(lme8))
Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: rating ~ (1 | tid.f) + (1 | obsorder.f) + (1 | comp.f) + (1 |  
    tid.f:comp.f) + (1 | obsorder.f:comp.f) + (1 | tid.f:obsorder.f)
   Data: ratings
 Subset: ratings$bins == quantile

     AIC      BIC   logLik deviance df.resid 
 15758.1  15817.7  -7871.0  15742.1    12772 

Scaled residuals: 
    Min      1Q  Median      3Q     Max 
-3.6535 -0.3833  0.1362  0.5225  3.4610 

Random effects:
 Groups            Name        Variance  Std.Dev.
 tid.f:comp.f      (Intercept) 0.0192757 0.13884 
 tid.f:obsorder.f  (Intercept) 0.0421666 0.20535 
 tid.f             (Intercept) 0.0000000 0.00000 
 obsorder.f:comp.f (Intercept) 0.0009318 0.03052 
 comp.f            (Intercept) 0.0173531 0.13173 
 obsorder.f        (Intercept) 0.0107521 0.10369 
 Residual                      0.1588568 0.39857 
Number of obs: 12780, groups: tid.f:comp.f, 3195; tid.f:obsorder.f,
1420; tid.f, 355; obsorder.f:comp.f, 45; comp.f, 9; obsorder.f, 5

Fixed effects:
            Estimate Std. Error t value
(Intercept)  2.85126    0.06774   42.09

For decile 5, I notice that the column fourth from the right goes to 0
after the first line. Does that mean something? Any ideas about why this
is failing?

Thanks in advance.


-- 
Stuart Luppescu -=-=- slu <AT> ccsr <DOT> uchicago <DOT> edu
CCSR at U of C ,.;-*^*-;.,  ccsr.uchicago.edu
     (^_^)/    才文と智奈美の父
[Crash programs] fail because they are based on the theory that, 
with nine women pregnant, you can get a baby a month.
                -- Wernher von Braun