[R-sig-ME] singular fit

[Jill Brouwer]

Dear Thierry,

There are 2 males and 2 females per block and 2 trials for each cross (so 8
trials in all) per treatment (pH). I have 16 blocks in total.

Fertilisation is binomial (success ie number of fertilised eggs out of 100)

I am trying to determine whether the random effects and their interactions
are significant in the model. I don’t understand what you mean by that last
point? Is there possibly a reference you could direct me to? Other
likelihood ratio tests I have done when removing male but keeping the
interaction have come up as significantly different to the full model ?
Sorry I am still quite new to all of this.

Kind regards,
Jill

On Thursday, January 2, 2020, Thierry Onkelinx <thierry.onkelinx using inbo.be>
wrote:

> 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
> <https://www.google.com/maps/search/Havenlaan+88?entry=gmail&source=g>
> 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 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
>>> <https://www.google.com/maps/search/Havenlaan+88?entry=gmail&source=g>
>>> 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
>>>>
>>>>         [[alternative HTML version deleted]]
>>>>
>>>> _______________________________________________
>>>> R-sig-mixed-models using r-project.org mailing list
>>>> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>>>>
>>>

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