singular fit

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 at 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 at inbo.be
Havenlaan 88 bus 73, 1000 Brussel
www.inbo.be


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Op do 2 jan. 2020 om 06:47 schreef Jill Brouwer <jilbo97 at 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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