[R-sig-ME] blme optimizer warnings

[Ben Bolker]

    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
>
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>
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