[R] Using of LME function in non-replicate data
spencer.graves at pdf.com
Tue Mar 21 03:39:39 CET 2006
You did not provide a simple, self-contained replicable example, so I
can not say for sure. You say you have 49 observations in 7 blocks.
The issue described in the post you cite would be a problem if you had
49 blocks of size 1. I've tried to fit models like that, only to find
after getting strange results that 'lme' did not bother to tell me how
stupid I was: It just tried to do what I asked.
If you've got 7 blocks with 7 treatments and 10-20 fixed effects, I
do not believe that the issue described in that post would be a problem
for you. You may have other problems, like too much noise to find the
signals you most care about. However, those are different from the
issue you cited.
hope this helps.
Cleber N.Borges wrote:
> Hello all R-users!
> In Jun-2005, I find the follow discussion about using
> LME function ( in NLME library ) for fitting
> non-replicate data
> The thread: ANOVA vs REML approach to variance
> component estimation
> Someone expose the follow problem:
> # non-replicate data
> y <- c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, 1.3, -0.3,
> 0.5, -1.4, -0.2, 1.8)
> ID <- factor( 1:12 )
> varcomp(lme(y ~ 1, random = ~ 1 | ID))
> # RESULTS:
> # ID Within
> # 1.6712661 0.2350218
> Prof. Dr. Douglas Bates reply this:
> > It's a spurious convergence in lme. There is no
> check in lme for the
> > number of observations exceeding the number of
> groups. There should
> > be. I'll add this to the bug reports list.
> But I have one similar problem and one doubt.
> I have 49 distinctive experiments split in 7 blocks (
> split plot design non-replicate )
> I fitting models with ~ 10 or ~ 20 coefficients (
> several responses. )
> it seems describe the data by
> experimental versus predicted responses plot and
> residuals plot
> My doubt: The components of variance given by LME
> function are
> reliable approximate estimates or this variance are
> spurious too?
> ... I thinked that this varinces were calculate by
> "lack of fit terms".
> In the case of this variances are wrong, even so can
> I use the REML coefficients estimates?
> Thanks in advanced!
> Chemistry student
> R-help at stat.math.ethz.ch mailing list
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