[R] lme - problems with model

[CG Pettersson]

Hello all!

I´m working with some training datasets in a SAS-based course, trying
to do the same things in lme that I do in PROC MIXED. 

Why don´t lme do an analysis on this dataset when I use the model
water*temp?
The trouble comes from the water:temp term, as it works with
water+temp.
The data are, indeed, assymetric but lm accepts the water:temp term
giving results in the F-test near what PROC MIXED produces. MIXED
accepts the model.

The water:temp term can be removed from the model according to the
F-test in SAS (and to the lm model without any random term). Doing so
in both MIXED and lme gives reasonably similar results for both
systems.

What do the error message mean, and how can I get around this?

/CG


The dataset:
> milk
   water temp rep maill4 maill6 maill8 taste4 taste6 taste8
1      1  100   1   2.90   2.13   2.39   10.1   10.0    9.6
2      1  100   2   2.19   2.20   2.27   11.0    9.3   11.0
3      1  100   3   2.13   2.20   2.41   10.1    7.0    9.6
4      1  110   1   2.13   2.34   2.41   11.0   10.5    9.8
5      1  110   2   2.32   2.27   2.25   11.0   11.3   11.2
6      1  110   3   2.13   2.34   2.42    9.4   10.7    9.0
7      1  120   1   2.00   2.49   2.71   11.1   11.2   11.4
8      1  120   2   2.41   2.49   2.46   11.6   11.7    9.6
9      1  120   3   2.22   2.49   2.73   10.7   10.3   10.2
10     2  100   1   2.13   2.41   2.49   11.1   10.8   11.2
11     2  100   2   2.49   2.34   2.53   11.1   11.2    9.2
12     2  100   3   2.80   2.63   3.33    8.3    9.7    7.8
13     2  120   1   2.38   2.85   2.06   11.9   11.2   11.2
14     2  120   2   2.61   2.70   2.70   11.7   10.8   11.0
15     2  120   3   2.77   3.06   3.25   10.9    9.0    9.4
16     2  140   1   2.56   2.84   3.10   10.7   11.2    9.8
17     2  140   2   2.63   2.61   2.81   10.8   11.0   11.6
18     2  140   3   2.99   3.28   3.75    9.2    9.6    9.6
19     3  100   1   2.60   2.24   2.32   10.8    8.4   10.8
20     3  100   2   2.06   2.11   2.20   11.0   11.2   11.8
21     3  100   3   1.98   2.34   2.80   10.3   10.2   10.6
22     3  110   1   1.91   2.06   2.29   11.0   11.4    9.4
23     3  110   2   1.98   1.98   2.15   10.0   11.8   10.6
24     3  110   3   1.98   2.51   2.81    9.3    9.2   10.2
25     3  140   1   2.27   2.42   2.72   10.8   11.6   12.0
26     3  140   2   2.27   2.20   2.41   11.2   11.0   11.4
27     3  140   3   2.20   2.77   3.06   10.5   10.2   10.0

The failing model:
> lme(maill6 ~ water * temp  , random= ~1|rep, data = milk)
Error in MEEM(object, conLin, control$niterEM) : 
        Singularity in backsolve at level 0, block 1

The smaller (working) model:
> lme(maill6 ~ water + temp  , random= ~1|rep, data = milk)
Linear mixed-effects model fit by REML
  Data: milk 
  Log-restricted-likelihood: 4.922178
  Fixed: maill6 ~ water + temp 
(Intercept)      water2      water3     temp110     temp120    
temp140 
 2.19466667  0.32800000 -0.04533333  0.07800000  0.32133333 
0.35066667 

Random effects:
 Formula: ~1 | rep
        (Intercept)  Residual
StdDev:   0.1477760 0.1323057

Number of Observations: 27
Number of Groups: 3 
> 




CG Pettersson, MSci, PhD Stud.
Swedish University of Agricultural Sciences
Dep. of Ecology and Crop Production. Box 7043
SE-750 07 Uppsala