The data set is as follows:
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 Ch? 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
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