[R-sig-ME] Question about linear mixed model

[Douglas Bates]

I recommend sending questions like this to the
R-SIG-Mixed-Models at R-project.org mailing list which I have cc:'d on this
reply.  Many of those who read that list can reply faster than I am able to.

On Thu, Sep 11, 2014 at 9:06 AM, Hung-Chih Ku <
Hung-Chih.Ku at utsouthwestern.edu> wrote:

>  Dear Dr. Bates,
>
>  My name is Hung-Chih Ku and I am a postdoctoral fellow at UT
> Southwestern Medical Center. I am trying to estimate variance components
> from a linear mixed model Y=Xa+Zb+e where Z is an nxp unstructured matrix.
> The following is an example of R code.
>
>   n <- 50
>
> p <- 5
>
> X <- runif(n)
>
> Z <- NULL
>
> for (i in 1:p) {
>
> Z <- cbind(Z, rbinom(n, 2, .3))
>
> }
>
> a <- 0.5
>
> b <- rnorm(p)
>
> e<- rnorm(n)
>
> Y <- X*a + Z*b + e
>
> library(nlme)
>
> group=rep(1,n)
>
>  fit <- lme(Y~X, random=list(group=pdIdent(~-1+Z)))
>
>  VarCorr(fit)
>
>  However, when n and p increase, let's say n=1000 and p=500, the lme will
> be taking a long time to estimate two variances (variances of b and e). Is
> there a way to speed up? Thank you for your time and I am looking forward
> to hearing from you.
>

When n=1000 and p=500 you are trying to estimate 500,000 random effects
coefficients from 1000 observations which seems a bit optimistic  You may
want to reexamine your model specification.  It doesn't make sense to me.

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