title: Why does an lmer model converge in one experimental condition but not another?
body: <p>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).</p>
  <pre><code>omnibus_model &lt;- lmer(Outcome ~ Var1*Var2*Cond + (Cond|ID) + (1|famID), data=df)
  
  </code></pre>
  <p>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.</p>
  <pre><code>condA &lt;- df[which(df$condition=='A'),]
  condA_model &lt;- lmer(Outcome ~ Var1*Var2 + (1|famID), data=condA)
  condB &lt;- df[which(df$condition=='B'),]
  condB_model &lt;- lmer(Outcome ~ Var1*Var2 + (1|famID), data=condB)
  </code></pre>
  <p>condA_model converges; condB_model does not. In condB_model &quot;famID (Intercept)&quot; 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.</p>
  <p>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?</p>