[R-sig-ME] Expected correlation in a mixed model

[Douglas Bates]

On Mon, Nov 22, 2010 at 9:07 AM, Ben Bolker <bbolker at gmail.com> wrote:
>  However: centering the concentration doesn't actually have much effect
> in this example (which it would if the remoteness from the origin were
> the problem), i.e.
>
> concsc <- scale(conc)
> (lmer1<-lmer(od~concsc+(concsc|run)))
>
>  This setup seems a little bit odd to me:
>  * the data set size is fairly small -- only 4 levels of the random
> effect ('run'), which often leads to this sort of collapse (zero
> variances and/or perfect correlations)

Exactly.  You are trying to estimate three variance components (two
variances and a covariance) from four groups.  That is not very much
information for what you are trying to estimate.

Bear in mind that it is generally more difficult to estimate a
variance or a covariance than it is to estimate a coefficient in a
linear predictor.  If you use the verbose=TRUE option you can see the
path of the iterations with respect to the three relative variance
component parameters. In lme4a the verbose output shows

> (lmer1<-lmer(od~conc+(conc|run), verbose=TRUE))
npt = 7 , n =  3
rhobeg =  0.2 , rhoend =  2e-07
   0.020:  12:     -2.66406; 1.07341 0.0671561  0.00000
  0.0020:  19:     -2.73319; 1.12766 0.161299  0.00000
 0.00020:  27:     -2.73356; 1.11840 0.161578  0.00000
 2.0e-05:  34:     -2.73357; 1.11786 0.161331 2.74933e-05
 2.0e-06:  40:     -2.73357; 1.11777 0.161302  0.00000
 2.0e-07:  44:     -2.73357; 1.11777 0.161301  0.00000
At return
 48:    -2.7335660:  1.11777 0.161300 1.10253e-08
Linear mixed model fit by REML ['merMod']
Formula: od ~ conc + (conc | run)
REML criterion at convergence: -2.7336

Random effects:
 Groups   Name        Variance Std.Dev. Corr
 run      (Intercept) 0.053559 0.23143
          conc        0.001115 0.03340  1.000
 Residual             0.042867 0.20704
Number of obs: 60, groups: run, 4

Fixed effects:
            Estimate Std. Error t value
(Intercept)  0.52654    0.12148   4.334
conc         0.92236    0.07701  11.977

Correlation of Fixed Effects:
     (Intr)
conc 0.001

The important thing about the iterations is that the first and third
parameters are constrained to be non-negative and the third parameter
immediately is driven to zero.

Both the REML and the ML criteria try to balance complexity of the
model versus the fidelity to the data.  It happens that the way that
the complexity is defined, the least complex models have a singular
variance-covariance matrix for the random effects.  Unless there is
sufficient information in the data to make a non-singular
variance-covariance matrix then the criterion will drive it to
singularity.

Also, as Ben notes you are not simulating from the model that you are
fitting.  You are simulating from a simpler model and that's what the
fit tends towards.

>  * there is no variation in slopes across runs (the only randomness
> here is the error term).  Perhaps what you're looking for is
>
> (lmer2<-lmer(od~concsc+(1|run) + (0+concsc|run)))
>
>  which fixes the correlation at zero.
>
>  * it's also the case here that the random effect on the intercept of
> 'run' is uniformly distributed, rather than normal -- I don't know if
> that would have an effect.
>
>  Ben Bolker
>
>
>
> On 11/22/2010 09:44 AM, Andrew Robinson wrote:
>> Yes indeed --- remoteness of the data from the origin is a plausible
>> explanation.
>>
>> Cheers
>>
>> Andrew
>>
>> On Mon, Nov 22, 2010 at 8:50 PM, S Ellison <S.Ellison at lgc.co.uk> wrote:
>>
>>> Forgive the possibly numb-brained question, but is there a reason why
>>> the correlation between random effects coefficients in lmer should come
>>> out as identically 1.0 in a model of the form
>>>
>>> lmer(x ~ a + (a|b) )
>>>
>>> ?
>>>
>>> An example:
>>> set.seed(403)
>>> require(lme4)
>>> run <- gl(4, 15)
>>> conc <- rep(rep(c(0,0.1, 0.2, 0.4, 1.0), 3), 4)
>>> boxplot(conc~run)
>>> offset=0.2*as.numeric(run)
>>> od <- offset+conc+rnorm(60, 0, 0.2)
>>> plot(conc, od)
>>>
>>> (lmer1<-lmer(od~conc+(conc|run)))
>>> VarCorr(lmer1)
>>>
>>>
>>> S Ellison
>>> LGC
>>>
>>> *******************************************************************
>>> This email and any attachments are confidential. Any u...{{dropped:21}}
>>
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