CODEWITHSUNDEEP
rlme4mixed-modelssingularmultilevel-analysis
bella1998
bella1998

Reputation: 13

Multilevel model using glmer: Singularity issue

I'm using R to run a logistic multilevel model with random intercepts. I'm using the frequentist approach (glmer). I'm not able to use Bayesian methods due to the research centre's policy.

When I run my code it says that my model is singular. I'm not sure why or how to fix the issue. Any advice would be appreciated!

More information about the multilevel model I used:

I'm using a multilevel modelling method used in intersectionality research called multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). The method uses individual level data as level 2 (the intersection group) and nests individuals within their intersections.

My outcome is binary and I have three categorical variables as fixed effects (gender, martial status, and disability). The random effect (level 2) is called intersect1 which includes each unique combination of the categorical variables (gender x marital x disability).

This is the code: MAIHDA_full <- glmer(IPV_pos ~ factor(sexgender) + factor(marital) + factor(disability) + (1|intersect1), data=Data, family=binomial, control=glmerControl(optimizer=”bobyqa”,optCtrl=list(maxfun=2e5)))

Upvotes: 1

Views: 533

Answers (1)

Robert Long
Robert Long

Reputation: 6812

The usual reason for a singular fit with mixed effects models is that either the random structure is overfitted - typically because of the inclusion of random slopes, or in the case such as this where we only have random intercepts, then the variation in the intercepts is so small that the model cannot detect it.

Looking at your model formula I suspect the issue is:

The random effect (level 2) is called intersect1 which includes each unique combination of the categorical variables (gender x marital x disability).

If I have understood this correctly, the model is equivalent to:

IPV_pos ~ sexgender + marital + disability + (1 | sexgender:marital:disability)

It is likely that any variation in sexgender:marital:disability is captured by the fixed effects, leading to near-zero variation in the random intercepts.

I suspect you will find almost identical results if you don't use any random effect.

Upvotes: 1

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