[R] question about returning Random Effects' covariance matrix estimate using lme fitting

[Chaofeng Kou]

Dear all

I am fitting and analyzing linear mixed-effects models using the
R command 'lme'. The following is the results:

dental.fit <- lme(fixed = distance~age, random = ~age + cluster
= ~subject, data = dental)

> summary(dental.fit)

Variance/Covariance Components Estimates:
                 Standard Deviation(s) of Random Effect(s)
                            (Intercept)        age
                            2.134464     0.1541247
                 Correlation of Random Effects
                                 (Intercept)
                        age    -0.6024329
                 Cluster Residual Variance: 1.716232

Fixed Effects Estimates:
                           Value       Approx. Std.Error    z ratio(C)
          (Intercept)     16.3406250     0.98005731       16.6731321
               age         0.7843750      0.08275189    9.4786353
               sex         1.0321023      1.53545472     0.6721802
              age:sex   -0.3048295      0.12964730      -2.3512218
    Conditional Correlations of Fixed Effects Estimates
               (Intercept)    age          sex
     age       -0.8801554
     sex       -0.6382847   0.5617897
     age:sex 0.5617897   -0.6382847    -0.8801554
I have known that using command 'dental.fit$varFix' I can obtain
the conditional covariance matrix of the fixed effects.
My question is how I can return the covariance matrix estimate of
the random effects. I tried many commands such as 'dental.fit$varRan',
'dental.fit$var.Ran', but they didn't work.

Thanks very much!

Chaofeng