[ windows  XP , R 2.12.2 , lme4 ]

This is probably a naive question.

I have a data set where 1 measurement is taken at each of 2
specific locations on each subject; these are considered to be 
random effects varying from subject to subject, but with  different 
variances for the two locations - the two measurements  on each 
subject are also highly correlated.

Data layout and formula are shown below - the measurement 
location is  specified by the factor 'Label'  .

Having fitted the mixed effects model,  I extracted the various 
random effects using ranef(.) and calculated their variance.covariance
matrices  ...  but these are not the same as, or even similar to, the 
variance.covariance matrices given by VarCorr(.) ...

Should they be the same ?

                thanks  Robert Kinley

Formula:
 
Hist.lmer<-lme4::lmer(Data ~ (Group*Label ) + 
                             (0+Label |Study) +
                             (0+Label |Study:Group)  +
                             (0+Label |Subject:Study:Group), 
 data=Hist)
 
Data Layout:

      Study   Group    Label        Data Subject
1   8221-631      QC log10Cer -1.33161408       1
73  8221-631      QC log10Hip -1.12378216       1
2   8221-631      QC log10Cer -1.00000000       2
74  8221-631      QC log10Hip -0.74232142       2
3   8221-631      QC log10Cer -1.26360350       3
75  8221-631      QC log10Hip -1.04817696       3
4   8221-631      QC log10Cer -1.25414480       4
76  8221-631      QC log10Hip -1.01367622       4
5   8221-631 Vehicle log10Cer -1.27654433       5
77  8221-631 Vehicle log10Hip -0.86966623       5
6   8221-631 Vehicle log10Cer -1.00921731       6
78  8221-631 Vehicle log10Hip -0.58838029       6
... etc
 

 
 

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