# Singular fit error caused by random factor

I'm trying to fit a generalised mixed model for some embryo data and I am getting the error for singular fit. my model is

m1=glmer(segregation ~ total + carrier.gender + (1 | couple/cycle), data=data1, family=binomial)


I can see the variance for both random factors is 0. If I take out the random factors of couple and cycle I do not get this error but for the sake of my experiment I can not exclude these factors. Some of the cycles only include 1 embryo while others have many. Is this the reason for the singular fit? If so is it OK to ignore this message or do I need to adjust my experiment somehow?

summary(m1)
Generalized linear mixed model fit by maximum likelihood
(Laplace Approximation) [glmerMod]
Family: binomial  ( logit )
Formula:
segregation ~ total + carrier.gender + (1 | couple/cycle)
Data: data1

AIC      BIC   logLik deviance df.resid
1138.7   1162.3   -564.3   1128.7      830

Scaled residuals:
Min      1Q  Median      3Q     Max
-1.5164 -1.0245  0.7200  0.8689  1.2760

Random effects:
Groups       Name        Variance Std.Dev.
cycle:couple (Intercept) 0        0
couple       (Intercept) 0        0
Number of obs: 835, groups:  cycle:couple, 242; couple, 141

Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept)         1.0982377  0.2591267   4.238 2.25e-05 ***
total              -0.0002428  0.0000856  -2.837  0.00456 **
carrier.genderMale -0.5164417  0.1428549  -3.615  0.00030 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Correlation of Fixed Effects:
(Intr) total
total       -0.934
crrr.gndrMl -0.169 -0.068
convergence code: 0
boundary (singular) fit: see ?isSingular


Thank you

You should not trust the results from a model that has not appropriately converged. Start from a simpler model including only the random intercepts for couples, and see if this converges and the variance is positive. Also, fit the model using the adaptive Gaussian quadrature, i.e., set nAGQ to 10 or 15.