[R-sig-ME] False Convergence warning

[Ben Bolker]

  Some quick points.

* the nlminb "false convergence" error is quite hard to troubleshoot
<https://stackoverflow.com/questions/40039114/r-nlminb-what-does-false-convergence-actually-mean>

* I would normally suggest scaling your variables as a cheap way to
improve robustness, but it looks like your variables are all effectively
binary/scaled anyway?

* the only part of your model that looks unusual/glmmTMB-specific is
dispformula=~0:  I assume you're doing this because otherwise some of
your variance terms are confounded with the residual variance?  You
could try fitting this model in blme::blmer, with the residual std dev
prior fixed to a small non-zero value (the std dev value below is
(.Machine$double.eps)^(0.25)), and see if you get the same results ...

* it should be possible, but isn't presently, to try glmmTMB with an
alternate optimizer (stay tuned: this is available on a development
branch https://github.com/glmmTMB/glmmTMB/tree/mapArgs at the moment ...)

On 2019-05-21 11:07 a.m., Robert Ackerman wrote:
> Hi everyone,
> 
> I have data from a speed-dating study, where groups of men and women
> interacted with each other for five minutes and subsequently provided
> ratings of the interaction and their partner.
> 
> These are the first lines of my data set:
> 
>    GROUPID MaleID FemaleID MF FM Agender AATTRACT
> 
> 1        1      1        2  1  0       1     3.00
> 
> 2        1      1        4  1  0       1     3.25
> 
> 3        1      1        6  1  0       1     6.00
> 
> 4        1      1        8  1  0       1     3.50
> 
> 5        1      1       10  1  0       1     3.50
> 
> 6        1      1        2  0  1      -1     5.00
> 
> 7        1      3        2  0  1      -1     3.50
> 
> 8        1      5        2  0  1      -1     5.00
> 
> 9        1      7        2  0  1      -1     4.75
> 
> 10       1      9        2  0  1      -1     2.25
> 
> 11       1      3        2  1  0       1     1.25
> 
> 12       1      3        4  1  0       1     3.25
> 
> 
> 
> Altogether, I have 904 observations (33 speed-dating groups comprised of
> 3-5 men and women each).
> 
> I’m trying to use glmmTMB to get a model with crossed random effects and an
> unstructured covariance matrix for the residuals to run. Please note that I
> was able to get this model to run in SPSS without problems (estimates of
> fixed effects and random effects are also virtually identical). However, I
> wanted to use R for its ease of checking multilevel modeling assumptions.
> 
> Here is the syntax for the model I tried to run in glmmTMB:
> 
> glmmTMB(AATTRACT ~ 1 + Agender+ (0 + MF + FM|GROUPID:MaleID) +
> 
>                    (0 + FM + MF|GROUPID:FemaleID)  + us(MF + FM + 0|
> GROUPID:MaleID:FemaleID),
> 
>                     data = SD_data,  family = gaussian(link = "identity"),
> dispformula=~0, REML = FALSE,
> 
>                    verbose = TRUE)
> 
> I received the following output and error message after running this model:
> 
> Formula: AATTRACT ~ 1 + Agender + (0 + MF + FM | GROUPID:MaleID) + (0 +
> 
>   FM + MF | GROUPID:FemaleID) + us(0 + MF + FM + 0 | GROUPID:MaleID:FemaleID)
> 
> Dispersion:                ~0
> 
> Data: SD_data
> 
>       AIC       BIC    logLik  df.resid
> 
>  2891.436  2944.311 -1434.718       893
> 
> Random-effects (co)variances:
> 
> 
> 
> Conditional model:
> 
>  Groups                  Name Std.Dev.  Corr
> 
>  GROUPID:MaleID          MF   0.5572656
> 
>                          FM   0.7916470 -0.02
> 
>  GROUPID:FemaleID        FM   0.6732786
> 
>                          MF   0.7529185 -0.16
> 
>  GROUPID:MaleID:FemaleID MF   0.8523621
> 
>                          FM   0.9419247 0.26
> 
>  Residual                     0.0001221
> 
> 
> 
> Number of obs: 904 / Conditional model: GROUPID:MaleID, 120;
> GROUPID:FemaleID, 120; GROUPID:MaleID:FemaleID, 452
> 
> 
> 
> Fixed Effects:
> 
> 
> 
> Conditional model:
> 
> (Intercept)      Agender
> 
>      3.9048       0.1112
> 
> Warning message:
> 
> In fitTMB(TMBStruc) :
> 
>   Model convergence problem; false convergence (8). See
> vignette('troubleshooting')
> 
> 
> 
> Because I used verbose = TRUE, I also received the following mgc values
> (I’m only including the last few lines here).
> 
> mgc: 5.077952e-08
> 
> outer mgc:  0.544794
> 
> iter: 1  value: -5887.447 mgc: 0.003153025 ustep: 1
> 
> iter: 2  value: -5887.447 mgc: 6.546202e-08 ustep: 1
> 
> mgc: 9.447453e-08
> 
> outer mgc:  0.2579731
> 
> iter: 1  value: -5887.346 mgc: 0.003152465 ustep: 1
> 
> iter: 2  value: -5887.346 mgc: 5.733777e-08 ustep: 1
> 
> mgc: 7.849359e-08
> 
> outer mgc:  0.2594169
> 
> 
> 
> Does anyone have any insights into what might be going wrong?
> 
> Thanks!
> Rob
> 
> 	[[alternative HTML version deleted]]
> 
> _______________________________________________
> R-sig-mixed-models using r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>