See the error message. Thanks.

a.fit<-lme(distance~age, data=aaa, random=~day/subject)
getVarCov(a.fit)
Error in getVarCov.lme(a.fit) :
  Not implemented for multiple levels of nesting


On Wed, Sep 10, 2008 at 9:53 PM, Lyman, Mark <Mark.Lyman@atk.com> wrote:

>  Take a look again at help(getVarCov).
>
>
>
> Mark Lyman, Statistician
>
> ATK Launch Systems
>
> mark.lyman@atk.com
>
> *From:* huang min [mailto:minhuangr@gmail.com]
> *Sent:* Tuesday, September 09, 2008 10:02 PM
> *To:* r-help@r-project.org
> *Cc:* dieter.menne@menne-biomed.de
> *Subject:* Re: extract variance components
>
>
>
> Hi,
>
>
>
> Thanks for all. I now know how to extract the \sigma's.
>
> For the unbalanced model y_{ijk}=x\beta+\alpha_i+\beta_{ij}+e_{ijk}
>
> i=1,2,\dots,a, j=1,2,\dots,b_i, k=1,2,\dots,n_{ij}
>
>
>
> How can I extract the variance matrix $V$? The variance for the ith group
> is also of help. Suppose the ith group has totally 10 observations. b_i=4,
> n_{i1}=1,n_{i2}=3,n_{i3}=2 and n_{i4}=1. $V_i=\sigma_a^2 J_{10}+\sigma_b^2
> diag(J_1,J_3,J_2, J_1)+\sigma_e^2 I_{10}$, where I is the identity matrix
> and J_d is the matrix of 1's with dimention d \by d. J_d reduces to 1 if
> d=1. I only know how to extract the design matrix for the fixed effect by
> model.matrix(lme.fit2). How to deal with the parts for the random effects?
> Thank you.
>
>
>
> Huang
>
>
>
>
>
>
> On Fri, Aug 29, 2008 at 11:30 AM, huang min <minhuangr@gmail.com> wrote:
>
> HI,
>
>
>
> I would like to extract the variance components estimation in lme function
> like
>
>
>
> a.fit<-lme(distance~age, data=aaa, random=~day/subject)
>
>
>
> There should be three variances \sigma_day, \sigma_{day %in% subject } and
> \sigma_e.
>
>
>
> I can extract the \sigma_e using something like a.fit$var. However, I
> cannot manage to extract the first two variance components. I can only see
> the results in summary(a.fit).
>
>
>
> I have some problem in the lme4 package and hence use the nlme package. The
> example data also has some problem so I just list the function here using
> some imaginary data set. Thank you.
>
>
>
> Huang
>
>
>

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