Extracting variances of the estimated variance components in lme4

Dear all,

How does one extract extracting variances of the variance components in
lme4?
vcov(model) only gives the covariance matrix of fixed part of the
fitted model,
while VarCorr(model) only gives the estimated variance components
without their corresponding standard errors.
Yes the standard errors are asymptotic but how does one extract them
from the fit?

many thanks,
Freedom

Ben Bolker <bbolker at gmail.com> 2012/05/03 02:31 PM >>>

Angelina Mukherjee <angelina.mukherjee88 at ...> writes:

I have response measures corresponding to 2 patients. The structure

is as

follows:

Patient 1: ?Region 1 ? ? ? ? ? ? Region 2 ? ? ? ? ? ? ?Region 3
? ? ? ? ? ? ? ?S1 S2 S3 S4 ? ? ? S1 S2 S3 S4 ? ? ? ?S1 S2 S3 S4

Patient 2: ?Region 1 ? ? ? ? ? ? ? Region 2 ? ? ? ? ? ?Region 3
? ? ? ? ? ? ? ? S1 S2 S3 S4 ? ? ? S1 S2 S3 S4 ? ? ? ?S1 S2 S3 S4

?Hmm. ?Do you really have only two patients, i.e. a total of
24 response values? ?I understand that you're trying to do a
variance decomposition here (no fixed effects, only random
effects), but your estimates of variance will be extremely
inaccurate based on only two patients (you might want to consider
making patient a fixed effect, then you would at least have
6 data points (5 df) for the patient:region variance ...

Each patient has 3 regions and each region has 4 sub-regions.

(nested

design)

Fitting ? *lme( Response ~ 1, random=~ Patient + Region +Subregion |
Patient/Region/Subregion )*
allows me to specify covariance structure for the 'sub-region' term.

But I'm trying to fit a random effects model of the form as I have

only 1

observation per 'sub-region':
*lme( Response ~ 1, random=~ Patient + Region | Patient/Region )*

Is there a way I can specify a covariance structure like
the auto-regressive (to specify that correlation decreases with

distance as

one moves from Subregion 1 to Subregion 4) for the 'sub-region' term

only

as it is not included in my random effects model but I'd like to

account

for the correlation in it?

? I would think that something like

lme(Response~1, random = ~1|Patient/Region,
? correlation=corAR1(~Subregion))

?But I also think you're fitting a more complicated model
than can really be supported by 24 data points ...

?Ben Bolker