[R-sig-ME] Issue with boundary (singular) fit: see ?isSingular

[Sasha Vasconcelos]

Hi again,

Just an update. I received this reply about the strange warning (Check
model convergence: log-likelihood estimates lead to negative Chi-squared!)

Yes, the convergence issues will lead to non-observable Chi-squared. If you
remove those random components with variance close to 0, it should help.



On Mon, 4 Oct 2021 at 15:23, Sasha Vasconcelos <
sasha.m.vasconcelos using gmail.com> wrote:

> If there are only two years, it's not surprising that you'll get
> estimates of zero variance for (1|Year).  I would probably make Year a
> fixed effect.
> I also tried that, leaving only Point as a random effect. But I still get
> the singularity warning. Could it be that the sample size is simply too
> small to handle any sort of random structure..?
>
>
>    I can't find this warning message anywhere, even in the development
> branch of piecewiseSEM:
>
> https://github.com/jslefche/piecewiseSEM/search?q=convergence
>
> ??
>
>  I also haven't been able to find anything about that warning message
> anywhere, so I've posted this same question to
> jslefche <https://github.com/jslefche>/piecewiseSEM
> <https://github.com/jslefche/piecewiseSEM> on github and am hoping for an
> answer soon.
>
> On Mon, 4 Oct 2021 at 14:16, Ben Bolker <bbolker using gmail.com> wrote:
>
>>
>>
>> On 10/4/21 10:05 AM, Sasha Vasconcelos wrote:
>> > Hi,
>> >
>> > I'm running a piecewise SEM with 3 component models:
>> >
>> > lmer(response variable1 ~ predictors + (1|Point) + (1|Year),
>> input_table)
>> >
>> > glmer(response variable2 ~ predictors + (1| Point) + (1|Year), family =
>> > "binomial", input_table)
>> >
>> > glmer(response variable3 ~ predictors + (1| Point) + (1|Year), family =
>> > "binomial", input_table)
>> >
>> > Because sampling involved visiting 18 points in spring of 2018 and
>> again in
>> > spring of 2019, I specified samping point and year as random effects.
>>
>>    If there are only two years, it's not surprising that you'll get
>> estimates of zero variance for (1|Year).  I would probably make Year a
>> fixed effect.
>>
>> >
>> > When I run the model, this warning message appears:
>> > Check model convergence: log-likelihood estimates lead to negative
>> > Chi-squared!
>>
>>    I can't find this warning message anywhere, even in the development
>> branch of piecewiseSEM:
>>
>> https://github.com/jslefche/piecewiseSEM/search?q=convergence
>>
>> ??
>>
>> >
>> > This message also appears:
>> > boundary (singular) fit: see ?isSingular
>> >
>> >  From what I've read about the second message, it could be due to random
>> > effect variance estimates of zero. I checked and this happens in the 1st
>> > and 3rd component models. In the 1st model "Point" has zero variance,
>> and
>> > in the 3rd model "Year" has zero variance.
>> >
>> > My first question is (and I apologize in advance if this is silly to
>> ask)
>> > whether this means that there's not really an effect coming from Point
>> in
>> > component model 1 and from Year in component model 2? If so, would it be
>> > possible to remove those random effects to end up with:
>> >
>> > lmer(Response variable1 ~ Predictors + (1|Year), input_table)
>> >
>> > glmer(Response variable2 ~Predictors + (1| Point) + (1|Year), family =
>> > "binomial", input_table)
>> >
>> > glmer(Response variable3 ~ Predictors + (1| Point), family = "binomial",
>> > input_table)
>>
>>    Seems reasonable.
>> >
>> > My second question is whether the warning "Check model convergence:
>> > log-likelihood estimates lead to negative Chi-squared!" is related to
>> these
>> > singularity issues?
>> >
>> > Oh and I am using the development version of the piecewise SEM package
>> > installed using devtools. This is because this version provides
>> additional
>> > standardized coefficients for GLMM.
>> >
>> >
>> > Thanks!
>> >
>> >
>>
>> --
>> Dr. Benjamin Bolker
>> Professor, Mathematics & Statistics and Biology, McMaster University
>> Director, School of Computational Science and Engineering
>> Graduate chair, Mathematics & Statistics
>>
>> _______________________________________________
>> R-sig-mixed-models using r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>
>
>
> --
> Sasha Vasconcelos
>
> PhD student
> CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources,
> Associate Laboratory
> Instituto Superior de Agronomia
> Tapada da Ajuda
> 1349-017 Lisbon, Portugal
>
> ResearchGate <https://www.researchgate.net/profile/Sasha_Vasconcelos>
> ResearcherID
> <https://publons.com/researcher/2593829/sasha-vasconcelos/publications/>
>
>

-- 
Sasha Vasconcelos

PhD student
CIBIO/InBIO, Research Center in Biodiversity and Genetic Resources,
Associate Laboratory
Instituto Superior de Agronomia
Tapada da Ajuda
1349-017 Lisbon, Portugal

ResearchGate <https://www.researchgate.net/profile/Sasha_Vasconcelos>
ResearcherID
<https://publons.com/researcher/2593829/sasha-vasconcelos/publications/>

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