[R-sig-ME] singular fit

[Thierry Onkelinx]

Dear Jill,

I presume you use different males and females for each block? How many
blocks? How many trials per block (success + failure)? Is fertilisation a
discrete variable?

Removing a main (random) effect like (1|Male) while keeping interactions
(1|Male:Female) doesn't make sense. You'll get exactly the same model fit
with a different parametrisation as the interaction will model the main
effect.

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

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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
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Op do 2 jan. 2020 om 10:09 schreef Jill Brouwer <jilbo97 using gmail.com>:

> Sorry here is some more information:
>
> My research is looking at whether ocean acidification affects patterns of
> gamete compatibility between individual male/female mussels.
> Here I am looking at whether the ph of the fertilisation assays also
> influences male by female interactions.
>
> The design consists of two males and two females, crossed in every
> combination (so a total of four combinations) per block, with two
> replicates in each.
> There is a fixed effect of Fertilisation pH (just called Fertilisation
> below)
> Random effects are individual males and females (each assigned a unique
> number, but specified as a factor for the model), and block.
>
> the full model formula is this (which doesn't give the singular fit error):
> fertphmodel <- glmer(cbind(Success,Failure) ~ Fertilisation + (1|Block) +
> (1|Male) + (1|Female) + (1|Male:Female) +
>                       (1|Male:Fertilisation) + (1|Female:Fertilisation) +
> (1|Male:Female:Fertilisation),
>                     family = "binomial", data = fertph)
>
> I am using likelihood ratio testing to determine significance of the
> random effects, however when I create the reduced model with (1|Male)
> removed, and also the one for (1|Male:Female) removed, it spits out the
> singular fit error. (Formulas below). I was also reading about boundary
> effect problems with likelihood ratio testing, and am unsure how to account
> for this?
>
> fertphmodel1 <- glmer(cbind(Success,Failure) ~ Fertilisation + (1|Block) +
> (1|Female) + (1|Male:Female) +
>                         (1|Male:Fertilisation) + (1|Female:Fertilisation)
> + (1|Male:Female:Fertilisation),
>                       family = "binomial", data = fertph)
>
> fertphmodel3 <- glmer(cbind(Success,Failure) ~ Fertilisation + (1|Block) +
> (1|Male) + (1|Female) +
>                        (1|Male:Fertilisation) + (1|Female:Fertilisation) +
> (1|Male:Female:Fertilisation),
>                      family = "binomial", data = fertph)
>
> For fertphmodel1, the summary output says that the female random effect
> has an extremely low variance  (possibly a reason for singular fit?)
> var: 7.070e-10 sd: 2.659e-05
>
> And for fertphmodel3, the summary output says the Female:Fertilisation has
> a very low variance
> var 3.325e-10 sd 1.823e-05
>
> However, in the full model the all of the variances of the random effects
> are between 0.03 and 0.6.
>
> Hopefully this helps a bit !
>
> Thankyou,
> Jill
>
> On Thu, Jan 2, 2020 at 4:47 PM Thierry Onkelinx <thierry.onkelinx using inbo.be>
> wrote:
>
>> Dear Jill,
>>
>> Can you share the model formula and the design of your experiment? It's
>> hard to answer your question without such basic information.
>>
>> 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
>>
>> ///////////////////////////////////////////////////////////////////////////////////////////
>>
>> <https://www.inbo.be>
>>
>>
>> Op do 2 jan. 2020 om 06:47 schreef Jill Brouwer <jilbo97 using gmail.com>:
>>
>>> Hi all,
>>>
>>> I have fitted a GLMM using glmer in lme4, and when I run the model it
>>> comes
>>> out with a singular fit warning.
>>>
>>> However when I ran the isSingular command on it and changed the tolerance
>>> to 1e-05 instead of the default 1e-04 that caused the original warning,
>>> it
>>> comes out as false - no singular fit warning!
>>>
>>> Does this mean that the first warning is a false positive?
>>> I can't find anything that suggests what the tolerance ratio should be
>>> but
>>> in the GLMM FAQ on github, the troubleshooting example uses 1e-05.
>>>
>>> Is it fine to stay with this model - I would prefer it to include all the
>>> random effects as they are all of interest to me, and the model itself is
>>> structured based on how I ran my experiment.
>>>
>>> Sorry if this is a basic question, I am still learning!
>>>
>>> Kind regards,
>>> Jill
>>>
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>>>
>>> _______________________________________________
>>> R-sig-mixed-models using r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>
>>

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