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CG Pettersson

2004-Feb-23 09:52 UTC

[R] lme - problems with model

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

Douglas Bates

2004-Feb-23 13:26 UTC

head link

[R] lme - problems with model

CG Pettersson <cg.pettersson at evp.slu.se> 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
> 
> ______________________________________________
> R-help at stat.math.ethz.ch 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                            bates at stat.wisc.edu
Statistics Department                    608/262-2598
University of Wisconsin - Madison        http://www.stat.wisc.edu/~bates/

CG Pettersson

2004-Feb-23 15:20 UTC

head link

[R] lme - problems with model

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 <cg.pettersson at evp.slu.se> 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
> > 
> > ______________________________________________
> > R-help at stat.math.ethz.ch 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                            bates at stat.wisc.edu
> 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

Spencer Graves

2004-Feb-23 15:41 UTC

head link

[R] lme - problems with model

If you want to try to get the same answers as PROC MIXED, I 
suggest you try to figure out how SAS codes interactions and which ones 
it retains.  Then you can try code those manually and include them as 
separate explanatory variables, e.g., I((water=="2")&(temp==110)).
You
could work this out in "lm" then try the result on "lme". 

      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 <cg.pettersson at evp.slu.se> 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
>>>
>>>______________________________________________
>>>R-help at stat.math.ethz.ch 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                            bates at stat.wisc.edu
>>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
>
>______________________________________________
>R-help at stat.math.ethz.ch 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
>  
>

Dick Beyer

2004-Feb-24 18:45 UTC

head link

[R] lme - problems with model

As Spencer Graves suggested, I tried this with continuous variables.  Seems to
work ok:
> lme(maill6 ~ water * temp  , random= ~1|rep, data = milk)Linear mixed-effects model fit by REML
  Data: milk 
  Log-restricted-likelihood: -10.57237
  Fixed: maill6 ~ water * temp 
 (Intercept)        water         temp   water:temp 
-1.107227891  0.928965420  0.032507653 -0.008792517 

Random effects:
 Formula: ~1 | rep
        (Intercept)  Residual
StdDev:   0.1358565 0.2189339

Number of Observations: 27
Number of Groups: 3

For the smaller model, I get:
> lme(maill6 ~ water + temp  , random= ~1|rep, data = milk)Linear mixed-effects model fit by REML
  Data: milk 
  Log-restricted-likelihood: -8.068963
  Fixed: maill6 ~ water + temp 
(Intercept)       water        temp 
 1.17083333 -0.05819444  0.01212500 

Random effects:
 Formula: ~1 | rep
        (Intercept)  Residual
StdDev:   0.1328748 0.2348303

Number of Observations: 27
Number of Groups: 3 

Cheers,
Dick
*******************************************************************************
Richard P. Beyer, Ph.D.	University of Washington
Tel.:(206) 616 7378	Env. & Occ. Health Sci. , Box 354695
Fax: (206) 685 4696	4225 Roosevelt Way NE, # 100
			Seattle, WA 98105-6099
http://depts.washington.edu/ceeh/ServiceCores/FC5/FC5.html
*******************************************************************************

Message: 14
Date: Mon, 23 Feb 2004 07:41:07 -0800
From: Spencer Graves <spencer.graves at pdf.com>
Subject: Re: [R] lme - problems with model
To: CG Pettersson <cg.pettersson at evp.slu.se>
Cc: Douglas Bates <bates at stat.wisc.edu>, r-help at stat.math.ethz.ch
Message-ID: <403A1F13.7000008 at pdf.com>
Content-Type: text/plain; charset=ISO-8859-1; format=flowed

      If you want to try to get the same answers as PROC MIXED, I 
suggest you try to figure out how SAS codes interactions and which ones 
it retains.  Then you can try code those manually and include them as 
separate explanatory variables, e.g., I((water=="2")&(temp==110)).
You
could work this out in "lm" then try the result on "lme". 

      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 <cg.pettersson at evp.slu.se> 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
>>>
>>>______________________________________________
>>>R-help at stat.math.ethz.ch 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                            bates at stat.wisc.edu
>>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
>
>______________________________________________
>R-help at stat.math.ethz.ch 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
>  
>

R help - Feb 2004 - lme - problems with model

[R] lme - problems with model

[R] lme - problems with model

[R] lme - problems with model

[R] lme - problems with model

[R] lme - problems with model