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I am new to using linear mixed-effects models. I have a dataset where participants (ID, N = 7973) completed two experimental conditions (A and B). A subset of participants are siblings and thus nested in families (famID, N = 6908).

omnibus_model <- lmer(Outcome ~ Var1*Var2*Cond + (Cond|ID) + (1|famID), data=df)

The omnibus model converges and indicates a significant three way interaction between Var1, Var2 and Cond. As a post-hoc, to better understand what is driving the omnibus model effect, I subsetted the data so that there is only one observation per ID.

condA <- df[which(df$condition=='A'),]
condA_model <- lmer(Outcome ~ Var1*Var2 + (1|famID), data=condA)
condB <- df[which(df$condition=='B'),]
condB_model <- lmer(Outcome ~ Var1*Var2 + (1|famID), data=condB)

condA_model converges; condB_model does not. In condB_model "famID (Intercept)" variance is estimated at 0. In the condA_model, I get a small, but non-zero estimate (variance=0.001479). I know I could get an estimate of the fixed effect of interest in condition A versus B by a different method(such as randomly selecting one sibling per family for the analysis and not using random effects), but I am concerned that this differential convergence pattern may indicate differences between the conditions that would influence the interpretation of the omnibus model effect.

What difference in the two conditions could causing the model in one subset not to converge? How would I test for the possible differences in my data? Shouldn't the random effect of famID be identical in both subsets and thus equally able to be estimated in both post-hoc models?

Robert Long
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Claire_Laubacher
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1 Answers1

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As a post-hoc, to better understand what is driving the omnibus model effect, I subsetted the data so that there is only one observation per ID.

This procedure does not make sense.

What difference in the two conditions could causing the model in one subset not to converge?

There are many reasons. For one thing, these reduced datasets are, well, reduced, ie smaller, so there is far less statistical power to detect the "effects" that you are interested in, such as a variance of a random effect. In such cases, it may be estimated as zero and result in a singular fit.

Shouldn't the random effect of famID be identical in both subsets and thus equally able to be estimated in both post-hoc models?

No, these are completely different models, since the underlying data are different. There is no reason to expect the same estimates from both models.

Robert Long
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