# [R] crossed random effects

Spencer Graves spencer.graves at pdf.com
Tue Jul 1 16:38:17 CEST 2003

```	  Have you studied Pinhiero and Bates (2000) Mixed Effects Models in S
and S-Plus (Springer)?

Also, have you tried simplifying your "lme" call until you get
something that works, then start adding back terms in various
configurations until it breaks?

Have you tried to compute how many coefficients are estimated in both
fixed and random terms and evaluate whether all are estimable?  For
example, with 2 factors at 2 levels each, if you don't have all 4
possible combinations, you can't estimate the interaction -- even if you
have thousands of replications of each.

Finally, you can always try to read the code.  I've learned a lot
about S-Plus / R by doing that -- and solved a lot of my own problems
that way.

hope this helps.  spencer graves

Sarah Mclean wrote:
> Hi,
>
> if I have posted this twice, please ignore this. I'm
> not sure if I sent it to the correct e-mail address
> the first time.
>
> I have a data set on germination and plant growth with
> the following variables:
>
> dataset=fm
> mass (response)
> sub (fixed effect)
> moist (fixed effect)
> pop (fixed effect)
> mum (random effect nested within population)
> iheight (covariate)
> plot (random effect- whole plot factor for split-plot
> design).
>
> I want to see if moist or sub interacts with mum for
> any of the pops, but I am getting an error message.
>
> This is the formula I used:
> fm\$pmu <- getGroups(fm, ~1|pop/mum, level=2)
> fm\$grp = as.factor(rep(1,nrow(fm)))
> fm\$pl <- getGroups(fm, ~1|plot)
> fm\$mo <- getGroups(fm, ~1|moist)
> fm\$su <- getGroups(fm, ~1|sub)
>
>>fm1 <- lme(sqrt(mass) ~ iheight + moist*sub*pop,
>
> data=fm, random=list(grp=pdBlocked(list(pdIdent(~pl -
> 1), pdIdent(~pmu - 1),  pdIdent(~pmu:su - 1),
> pdIdent(~pmu:mo - 1)))))
> Error in chol((value + t(value))/2) : non-positive
> definite matrix in chol
>
> I know the problem is with the random interaction
> terms, but I don't know how to overcome this.
>
> Any advice would be greatly appreciated. I'm new to R
> and analysis such as this.
>
> Thank you,
>
> Sarah Mclean
> sarahmclean9 at yahoo.co.nz
>
>
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