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: > > > 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? > > Because of missing cells in the design > > > 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 interaction > term but only three coefficients are estimated. > > 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 during > the 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 > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > > -- > Douglas Bates [EMAIL PROTECTED] > Statistics Department 608/262-2598 > University of Wisconsin - Madison http://www.stat.wisc.edu/~bates/ > CG Pettersson, MSci, PhD Stud. Swedish University of Agricultural Sciences Dep. of Ecology and Crop Production. Box 7043 SE-750 07 Uppsala ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
