[R-sig-ME] Unidentifiable model in lmer
Thierry Onkelinx
thierry.onkelinx at inbo.be
Fri Sep 18 16:41:49 CEST 2015
Dear Takahiro,
Please send the output of dput(dataset). That's a lot easier to
copy-paste into an R session.
Best regards,
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 16:40 GMT+02:00 Takahiro Fushimi <taka.6765 op gmail.com>:
> 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 op 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 op 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 op 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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