[R-sig-ME] Errors in GLMER

Tue Jun 26 10:31:16 CEST 2018

Dear Michiel,

I think you need to take a step back. Your model is most likely too
complex for your data. You have about 16 parameters in your model and
only 103 observations... I tend to use as a rule of thumb: at least 10
effective observations per parameter. In case of a binomial
distribution I count the number of success and the number of failures
and use the lowest of the two as number of effective observations. In
ideal circumstances, a near 50%-50% split, you have 50 effective
observations so you can use only 5 parameters... Fisher's quote in my
signature might be applicable.

A side note: although you can code a binomial response as a factor, it
is IMHO more clear to code it as a logical value. If you do want to
code it as a factor, make sure that you read the details of ?family.

Best regards,

ir. Thierry Onkelinx
Statisticus / Statistician

Vlaamse Overheid / Government of Flanders
INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
AND FOREST
Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
thierry.onkelinx using inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be

///////////////////////////////////////////////////////////////////////////////////////////
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
///////////////////////////////////////////////////////////////////////////////////////////

2018-06-25 18:33 GMT+02:00 Michiel Kiggen <michiel.kiggen using gmail.com>:
> Dear Thierry
>
> Thanks so much for comments, I really appreciate it.
>
> Based on your feedback, I'm running the following model now:
>
> model_poly_nc <- glmer(AoR ~ Offers_lin*(sFW*sMF)+ Offers_quad *(sFW*sMF) +
> (1 |ID)+ (0 + Offers_lin| ID)+(0 + Offers_quad| ID),family = binomial, data
> = data, control = glmerControl(optCtrl = list(maxfun = 1e+9)))
>
> Offers_lin & Offers_quad are the trial variables (polynomials for linear and
> quadratic patterns). Both of them are centered. Metric is from 1 till 10. (1
> being trial in which people get lowest offer and 10 being the highest offer
> in an ultimatum game)
> sFW & sMF are the continous questionairre sumscores, standardized &
> centered.
> DV (2 level factor accept or reject)
> n = 103
>
> I ran the model with covariance terms for the random effects. It didn't
> converge, neither with optimizers bobyqa & Nelder_Mead.
> However, the function allFit, strangely returned [OK] for bobyqa,
> Nelder_Mead, nloptwrap.NLOPT_LN_NELDERMEAD & nloptwrap.NLOPT_LN_BOBYQA.
> The model above is without covariance terms for the random effects. After
> running this I get the same convergence issue and the same output on allFit:
> [OK] for bobyqa, Nelder_Mead, nloptwrap.NLOPT_LN_NELDERMEAD &
> nloptwrap.NLOPT_LN_BOBYQA.
>
> Using the getME function to look at the different Fixed Effects generated by
> allFit (no covariance term model, as described above), it reveals that the
> numbers are similar for bobyqa,  loptwrap.NLOPT_LN_NELDERMEAD &
> nloptwrap.NLOPT_LN_BOBYQA. While for the Nelder_mead numbers are just
> slightly different. Like a 0.02 difference.
>
> For the seperate Models with optimizers Bobyqa & Nelder_Mead (both with
> convergence issues), the fixed effects were exactly the same.
>
> I'm not sure how to interpret these allFit function results. For what
> patterns should I look incase I want to conclude these warnings are
> false-positives?
> Else, what options do I have? I've already stripped the model of it random
> covariances (Removing the random slope will make the purpose of using Mixed
> Models dispensable).
>
> My apologies if I'm asking any obvious questions. First time i'm running
> this kind of analysis.
>
> Any help is much appreciated!
>
> Kind regards,
>
> Michiel
>
>
>
>
>
> Op ma 25 jun. 2018 om 09:08 schreef Thierry Onkelinx
> <thierry.onkelinx using inbo.be>:
>>
>> Dear Michiel,
>>
>> Does it run with the random slope for trial. If I understand the
>> design correctly, you have only one observation per trial and per ID.
>> In that case a random slope for trial as an (ordered) factor won't
>> work.
>>
>> Consider using trial as a continuous variable and use some polynomials
>> to model it.
>>
>> Best regards,
>>
>> ir. Thierry Onkelinx
>> Statisticus / Statistician
>>
>> Vlaamse Overheid / Government of Flanders
>> INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE
>> AND FOREST
>> Team Biometrie & Kwaliteitszorg / Team Biometrics & Quality Assurance
>> thierry.onkelinx using inbo.be
>> Havenlaan 88 bus 73, 1000 Brussel
>> www.inbo.be
>>
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>> 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
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>>
>>
>>
>> 2018-06-24 23:41 GMT+02:00 Michiel Kiggen <michiel.kiggen using gmail.com>:
>> > Dear Reader,
>> >
>> > I'm trying to run a GLMER model for the following data:
>> > *2x scaled continous predictor* (sum score of 2 questionairres)
>> > *1x predictor being 10 trials* on a ultimatum game of which each trial
>> > is 1
>> > out of 10 possible options. (offer of a split of $20: e.g. you 1 and 19
>> > me). Inserted this a non ordered factor (10-levels) with sum-to-zero
>> > coding
>> > (contrast.sum).
>> > *1x dependent binary variable *being the response to the 10 trials
>> > valued
>> > at accepted (1) or reject (2). Entered as a factor.
>> >
>> > After the following model without correlations terms (I ran this model
>> > after failing to converge on a model without optimizers and the all_fit
>> > of
>> > that) I get the following errors:
>> >
>> > glmer(AoR ~ Trials * (sPredictor1*sPredictor2) + (1 | ID )+  (0 + Trials
>> > |ID),family = binomial, data = data, control = glmerControl(optCtrl =
>> > list(maxfun = 1e+9, optimizer = "bobyqa")))
>> >
>> >
>> > *fixed-effect model matrix is rank deficient so dropping 10 columns /
>> > coefficients*
>> > *Warning messages:*
>> > *1: In (function (npt = min(n + 2L, 2L * n), rhobeg = NA, rhoend = NA,
>> > :*
>> > *  unused control arguments ignored*
>> > *2: In (function (iprint = 0L, maxfun = 10000L, FtolAbs = 0.00001,
>> > FtolRel
>> > = 1e-15,  :*
>> > *  unused control arguments ignored*
>> > 3: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>> > :
>> >   unable to evaluate scaled gradient
>> > 4: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv,
>> > :
>> >   Model failed to converge: degenerate  Hessian with 11 negative
>> > eigenvalues
>> >
>> > I'm afraid I might be doing something wrong in handeling the DV or
>> > 10-factor level IV, which in turn, is causing the 3 errors in bold. Does
>> > anyone have suggestions. Or can some one tell me what the source of
>> > these
>> > errors are?
>> >
>> > Much obliged in advance,
>> >
>> > Kindest regards,
>> >
>> > Michiel Kiggen
>> >
>> >         [[alternative HTML version deleted]]
>> >
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