[R] Using of LME function in non-replicate data

[Cleber N.Borges]

 Hello all R-users!

 In Jun-2005, I find the follow discussion about using
of
 LME function ( in NLME library ) for fitting
non-replicate data

 The thread: ANOVA vs REML approach to variance
component estimation
 

http://tolstoy.newcastle.edu.au/R/help/05/06/6498.html


 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 )

 library(ape)
 library(nlme)
 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. 

 Alright!
 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! 
 Regards.

 Cleber
 Chemistry student