[R-sig-ME] blme optimizer warnings
Sijia Huang
hu@ng@jcc @end|ng |rom gm@||@com
Thu May 14 03:36:45 CEST 2020
Thanks for the quick reply, Ben!
I am replicating the Fernández-Castilla et al. (2018) article. Below are
the data they have in the article. Anything I can do to resolve the issue?
Thanks!
> meta
Study Outcome Subscale g Variance Precision
1 1 1 1 -0.251 0.024 41.455
2 2 1 1 -0.069 0.001 1361.067
3 3 1 5 0.138 0.001 957.620
4 4 1 1 -0.754 0.085 11.809
5 5 1 1 -0.228 0.020 49.598
6 6 1 6 -0.212 0.004 246.180
7 6 2 7 0.219 0.004 246.095
8 7 1 1 0.000 0.012 83.367
9 8 1 2 -0.103 0.006 162.778
10 8 2 3 0.138 0.006 162.612
11 8 3 4 -0.387 0.006 160.133
12 9 1 1 -0.032 0.023 44.415
13 10 1 5 -0.020 0.058 17.110
14 11 1 1 0.128 0.017 59.999
15 12 1 1 -0.262 0.032 31.505
16 13 1 1 -0.046 0.071 14.080
17 14 1 6 -0.324 0.003 381.620
18 14 2 6 -0.409 0.003 378.611
19 14 3 7 0.080 0.003 385.319
20 14 4 7 -0.140 0.003 385.542
21 15 1 1 0.311 0.005 185.364
22 16 1 1 0.036 0.005 205.063
23 17 1 6 -0.259 0.001 925.643
24 17 2 7 0.196 0.001 928.897
25 18 1 1 0.157 0.013 74.094
26 19 1 1 0.000 0.056 17.985
27 20 1 1 0.000 0.074 13.600
28 21 1 6 -0.013 0.039 25.425
29 21 2 7 -0.004 0.039 25.426
30 22 1 1 -0.202 0.001 1487.992
31 23 1 1 0.000 0.086 11.628
32 24 1 1 -0.221 0.001 713.110
33 25 1 1 -0.099 0.001 749.964
34 26 1 5 -0.165 0.000 6505.024
35 27 1 1 -0.523 0.063 15.856
36 28 1 1 0.000 0.001 1611.801
37 29 1 6 0.377 0.045 22.045
38 29 2 7 0.575 0.046 21.677
39 30 1 1 0.590 0.074 13.477
40 31 1 1 0.020 0.001 1335.991
41 32 1 1 0.121 0.043 23.489
42 33 1 1 -0.101 0.003 363.163
43 34 1 1 -0.101 0.003 369.507
44 35 1 1 -0.104 0.004 255.507
45 36 1 1 -0.270 0.003 340.761
46 37 1 1 0.179 0.150 6.645
47 38 1 2 0.468 0.020 51.255
48 38 2 4 -0.479 0.020 51.193
49 39 1 5 -0.081 0.024 42.536
50 40 1 1 -0.071 0.043 23.519
51 41 1 1 0.201 0.077 13.036
52 42 1 6 -0.070 0.006 180.844
53 42 2 7 0.190 0.006 180.168
54 43 1 1 0.277 0.013 79.220
55 44 1 5 -0.086 0.001 903.924
56 45 1 5 -0.338 0.002 469.260
57 46 1 1 0.262 0.003 290.330
58 47 1 5 0.000 0.003 304.959
59 48 1 1 -0.645 0.055 18.192
60 49 1 5 -0.120 0.002 461.802
61 50 1 5 -0.286 0.009 106.189
62 51 1 1 -0.124 0.006 172.261
63 52 1 1 0.023 0.028 35.941
64 53 1 5 -0.064 0.001 944.600
65 54 1 1 0.000 0.043 23.010
66 55 1 1 0.000 0.014 72.723
67 56 1 5 0.000 0.012 85.832
68 57 1 1 0.000 0.012 85.832
On Wed, May 13, 2020 at 6:00 PM Ben Bolker <bbolker using gmail.com> wrote:
> Without looking very carefully at this:
>
> * unless your response variable is somehow already centered at zero by
> design, a model with no intercept at all is going to be
> weird/problematic (random effects are always zero-centered by definition).
>
> * is it really OK to have an infinite scale in your wishart prior? (It
> may be fine, I'm not immediately familiar with the blme
> parameterizations, it just looks weird)
>
> * the fact that your standard devs are all exactly 1 suggests that the
> optimizer bailed out before actually doing anything (these are the
> default starting values).
>
> Can you provide a reproducible example?
>
> On 5/13/20 8:53 PM, Sijia Huang wrote:
> > Hi everyone,
> > I am fitting a cross-classified model with blme, but getting 1 optimizer
> > warning. The code and output are shown below. Any suggestions regarding
> > fixing the estimation issue? Thanks!
> >
> >
> >> meta.example <- blmer(g~0+(1|Study)+(1|Subscale)+
> > 1|Outcome:Study:Subscale),
> > + data=meta, weights = Variance,
> > + resid.prior = point(1),
> > + control = lmerControl(optimizer="bobyqa"))
> >
> >> meta.example
> > Cov prior : Outcome:Study:Subscale ~ wishart(df = 3.5, scale = Inf,
> > posterior.scale = cov, common.scale = TRUE)
> > : Study ~ wishart(df = 3.5, scale = Inf, posterior.scale =
> cov,
> > common.scale = TRUE)
> > : Subscale ~ wishart(df = 3.5, scale = Inf, posterior.scale =
> > cov, common.scale = TRUE)
> > Resid prior: point(value = 1)
> > Prior dev : NaN
> >
> > Linear mixed model fit by maximum likelihood ['blmerMod']
> > Formula: g ~ 0 + (1 | Study) + (1 | Subscale) + (1 |
> Outcome:Study:Subscale)
> > Data: meta
> > Weights: Variance
> > AIC BIC logLik deviance df.resid
> > Inf Inf -Inf Inf 64
> > Random effects:
> > Groups Name Std.Dev.
> > Outcome:Study:Subscale (Intercept) 1
> > Study (Intercept) 1
> > Subscale (Intercept) 1
> > Residual 1
> > Number of obs: 68, groups: Outcome:Study:Subscale, 68; Study, 57;
> > Subscale, 7
> > No fixed effect coefficients
> > convergence code 0; 1 optimizer warnings; 0 lme4 warnings
> >
> >
> >
> >
> > Best,
> > Sijia
> >
> > [[alternative HTML version deleted]]
> >
> > _______________________________________________
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>
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