[R-sig-ME] blme optimizer warnings
Sijia Huang
hu@ng@jcc @end|ng |rom gm@||@com
Thu May 14 04:04:01 CEST 2020
Here it is. Thanks!
A demonstration and evaluation of the use of cross-classified
random-effects models for meta-analysis
On Wed, May 13, 2020 at 6:57 PM Ben Bolker <bbolker using gmail.com> wrote:
>
> Can you give a more specific reference? I can't immediately guess from
> Fernández-Castilla's google scholar page which article it is ...
> On 5/13/20 9:36 PM, Sijia Huang wrote:
>
> 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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