An alternative would be to convert "temp" from a factor to a continuous variable. I would make plots of the response variables vs. "temp" with different lines and symbols for "water" and "rep" to make sure I had something that was mostly linear in some transformation of "temp".
hope this helps. spencer graves
CG Pettersson wrote:
Thanks a lot for the answer!
Now, I only have the last one left - How do I get round it? I knew about the missing cells in the design, but didn�t know how lme would react on them.
In this case, I can remove the water:temp term, but how can I be sure that this is the right thing to do?
Is the lm run without the random term enough for removing water:temp
from the lme model, or do I have to do a PROC MIXED run with the
random term to make that decision in a case like this?
Is it possible (for me) to understand why MIXED accepts the design but not lme? They ought to get the same sort of problems, or have I missed something?
/CG
-------------------
CG Pettersson <[EMAIL PROTECTED]> writes:trying
Hello all!
I�m working with some training datasets in a SAS-based course,
termto 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
sogiving 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
interactionBecause of missing cells in the designin both MIXED and lme gives reasonably similar results for both systems.
What do the error message mean, and how can I get around this?
xtabs(~water + temp, milk)temp
water 100 110 120 140
1 3 3 3 0 2 3 0 3 3 3 3 3 0 3
the model matrix for the fixed effects is rank deficient. In lm the rank deficiency is detected and appropriate adjustments made
coef(summary(lm(maill6 ~ water * temp, milk)))
Estimate Std. Error t value Pr(>|t|) (Intercept) 2.17666667 0.1142339 19.0544730 2.218661e-13 water2 0.28333333 0.1615511 1.7538308 9.647013e-02 water3 0.05333333 0.1615511 0.3301329 7.451108e-01 temp110 0.14000000 0.1615511 0.8665987 3.975669e-01 temp120 0.31333333 0.1615511 1.9395305 6.827304e-02 temp140 0.23333333 0.1615511 1.4443312 1.658280e-01 water3:temp110 -0.18666667 0.2284678 -0.8170371 4.245898e-01 water2:temp120 0.09666667 0.2284678 0.4231085 6.772282e-01 water2:temp140 0.21666667 0.2284678 0.9483467 3.555125e-01
Notice that you would expect 6 degrees of freedom for the
term but only three coefficients are estimated.during
In lme it is much more difficult to compensate for such rank
deficiencies because they could be systematic, like this, or they
could be due to relative precision parameters approaching zero
http://www.R-project.org/posting-guide.htmlthe iterations. Because of this we just report the error (although admittedly we could be a bit more explicit about the nature of the problem - we are reporting the symptom that we detect, not the probable cause).
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
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Douglas Bates [EMAIL PROTECTED]
Statistics Department 608/262-2598
University of Wisconsin - Madison
CG Pettersson, MSci, PhD Stud. Swedish University of Agricultural Sciences Dep. of Ecology and Crop Production. Box 7043 SE-750 07 Uppsala
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