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 at inbo.be
Havenlaan 88
<https://www.google.com/maps/search/Havenlaan+88?entry=gmail&source=g>
bus 73, 1000 Brussel
www.inbo.be
////////////////////////////////////////////////////////////
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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
////////////////////////////////////////////////////////////
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<https://www.inbo.be>
Op do 2 jan. 2020 om 10:09 schreef Jill Brouwer <jilbo97 at 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 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
<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 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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