[R-sig-ME] Unidentifiable model in lmer

Takahiro Fushimi taka.6765 at gmail.com
Fri Sep 18 16:40:10 CEST 2015


The data set is as follows:
 > dataset
            y A B  ID
1   60625.19 0 1  72
2   51088.38 0 1  73
3   67283.03 0 1  74
4   56550.54 0 1  75
5         NA 0 1  76
6         NA 0 1  78
7   58720.44 0 1  79
8   63939.01 0 1  86
9   66298.32 0 1  88
10  62662.15 0 1  89
11  53604.41 0 1  90
12  88338.55 0 1  91
13  62818.78 0 1  92
14  68696.71 0 1  93
15  57433.98 0 1  94
16  59105.99 0 1  95
17  44801.97 0 1  96
18  68655.48 0 1 121
19  62645.16 0 1 122
20  69409.15 0 1 123
21  43568.70 0 1 124
22  55693.21 1 1  77
23  57322.63 1 1  80
24  51095.55 1 1  81
25  57660.41 1 1  82
26  64128.63 1 1  83
27  55216.42 1 1  84
28  59945.17 1 1  98
29  67284.31 1 1 116
30  55401.13 1 1 117
31  60471.59 1 1 118
32        NA 1 1 119
33  63633.07 1 1 120
34  65939.14 1 1 125
35        NA 1 1 128
36  69488.38 2 1 100
37  43950.50 2 1 101
38  52782.99 2 1 102
39  55674.94 2 1 103
40  70130.25 2 1 104
41  72560.25 2 1 105
42  69297.95 2 1 106
43  53188.98 2 1 107
44  75687.53 2 1 108
45  68242.23 2 1 109
46  60696.15 2 1 110
47  65087.04 2 1 112
48  59377.71 2 1 113
49  55055.45 2 1 114
50  66673.40 2 1 115
51  65933.37 2 1 126
52  62636.56 2 1 127
53  49606.80 0 0  72
54  58668.13 0 0  73
55  56694.13 0 0  74
56        NA 0 0  75
57        NA 0 0  76
58  61928.95 0 0  78
59  63208.84 0 0  79
60  83786.71 0 0  86
61  72727.53 0 0  88
62  78873.45 0 0  89
63  47482.32 0 0  90
64  65967.84 0 0  91
65  53228.25 0 0  92
66  65699.59 0 0  93
67  61792.81 0 0  94
68  44816.72 0 0  95
69  71048.06 0 0  96
70  59671.84 0 0 121
71  89513.43 0 0 122
72  56972.30 0 0 123
73  45439.25 0 0 124
74  51357.83 1 0  77
75        NA 1 0  80
76  60146.86 1 0  81
77  56845.88 1 0  82
78  63181.13 1 0  83
79  63704.80 1 0  84
80  49140.22 1 0  98
81  54538.14 1 0 116
82  49411.32 1 0 117
83  66059.73 1 0 118
84  73147.55 1 0 119
85  55665.96 1 0 120
86  66821.27 1 0 125
87  66935.12 1 0 128
88  54350.63 2 0 100
89  48116.25 2 0 101
90  67664.66 2 0 102
91  64278.54 2 0 103
92  64555.03 2 0 104
93  62463.60 2 0 105
94  55831.53 2 0 106
95  57392.61 2 0 107
96  75727.00 2 0 108
97  64839.50 2 0 109
98  51009.78 2 0 110
99  65274.59 2 0 112
100 63339.91 2 0 113
101 62276.49 2 0 114
102 66159.42 2 0 115
103 58333.76 2 0 126
104 60275.09 2 0 127

Best regards,
Takahiro

On 9/18/15 3:23 AM, Thierry Onkelinx wrote:
> The formula shouldn't be the problem. model.matrix() is clever enough
> to handle this. Have a look at the examples below.
>
> model.matrix(~ A + B + A * B, data= data.frame(A = 1, B = 1))
> model.matrix(~ A + B + A * B + B + A:B + B:A, data= data.frame(A = 1, B = 1))
>
> I suggest to give use a reproducible example of the problem so that we
> can invesigate what when wrong.
> ir. Thierry Onkelinx
> Instituut voor natuur- en bosonderzoek / Research Institute for Nature
> and Forest
> team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
> Kliniekstraat 25
> 1070 Anderlecht
> Belgium
>
> To call in the statistician after the experiment is done may be no
> more than asking him to perform a post-mortem examination: he may be
> able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher
> The plural of anecdote is not data. ~ Roger Brinner
> The combination of some data and an aching desire for an answer does
> not ensure that a reasonable answer can be extracted from a given body
> of data. ~ John Tukey
>
>
> 2015-09-18 7:23 GMT+02:00 Ché Lucero <chelucero at uchicago.edu>:
>> I don't know if this is causing the problem you're seeing, but A*B expands
>> to A + B + A:B, so your model right now is R ~ A + B + A + B + A:B + (1|S).
>>
>> On Thu, Sep 17, 2015 at 5:53 PM Takahiro Fushimi <taka.6765 at gmail.com>
>> wrote:
>>
>>> Hi everyone,
>>>
>>> I have been working on a linear mixed effect model by using lmer()
>>> function, but I got an error saying that the fitted model is not
>>> identifiable.
>>>
>>> The data set includes the following variables:
>>> y = a numeric variable
>>> factorA = a 3-level categorical variable
>>> factorB = a 2-level categorical variable
>>> subjectID = subject id number. 2 measurements of y for each subject
>>>
>>> R code and output are as follows:
>>>   > (result <- lmer(y ~ factorA + factorB + factorA*factorB +
>>> (1|subjectID)))
>>> Linear mixed model fit by REML ['merModLmerTest']
>>> Formula: y ~ factorA + factorB + factorA * factorB + (1 | subjectID)
>>> REML criterion at convergence: 1928.966
>>> Random effects:
>>>    Groups    Name        Std.Dev.
>>>    subjectID (Intercept) 4711
>>>    Residual              7688
>>> Number of obs: 97, groups:  subjectID, 51
>>> Fixed Effects:
>>>         (Intercept)           factorA1           factorA2 factorB1
>>> factorA1:factorB1  factorA2:factorB1
>>>               62411              -2700              -1124
>>> -1037               1279               2482
>>>   > summary(result)
>>> Model is not identifiable...
>>> summary from lme4 is returned
>>> some computational error has occurred in lmerTest
>>> Linear mixed model fit by REML ['lmerMod']
>>> Formula: y ~ factorA + factorB + factorA * factorB + (1 | subjectID)
>>>
>>> REML criterion at convergence: 1929
>>>
>>> Scaled residuals:
>>>        Min       1Q   Median       3Q      Max
>>> -1.93423 -0.62611  0.01837  0.48887  2.73380
>>>
>>> Random effects:
>>>    Groups    Name        Variance Std.Dev.
>>>    subjectID (Intercept) 22194074 4711
>>>    Residual              59108495 7688
>>> Number of obs: 97, groups:  subjectID, 51
>>>
>>> Fixed effects:
>>>                     Estimate Std. Error t value
>>> (Intercept)          62411       2065  30.227
>>> factorA1             -2700       3238  -0.834
>>> factorA2             -1124       3008  -0.374
>>> factorB1             -1037       2512  -0.413
>>> factorA1:factorB1     1279       4013   0.319
>>> factorA2:factorB1     2482       3642   0.681
>>>
>>> Correlation of Fixed Effects:
>>>               (Intr) fctrA1 fctrA2 fctrB1 fA1:B1
>>> factorA1    -0.638
>>> factorA2    -0.687  0.438
>>> factorB1    -0.608  0.388  0.418
>>> fctrA1:fcB1  0.381 -0.601 -0.262 -0.626
>>> fctrA2:fcB1  0.420 -0.268 -0.606 -0.690  0.432
>>>   >
>>>
>>> Could anyone give me some idea of why this unidentifiability problem
>>> happens and how to fix it?
>>> Any help would be appreciated.
>>>
>>> Best regards,
>>> Takahiro
>>>
>>> _______________________________________________
>>> R-sig-mixed-models at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>          [[alternative HTML version deleted]]
>>
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-- 
Takahiro Fushimi
Columbia University in the City of New York
Master of Arts in Statistics



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