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I am computing the WAIC (widely applicable or Wataname-Akaike information criterion) using the waic() function from the 'loo' package in R. When I do so, I see a warning message as follows:

What should I do about this? I have noticed that several online examples/tutorials ignore this warning (e.g. here--this is a reproducible example). Is 5.1% (in my case) or 3.3% (in the example's case) a dangerously high number? How is this likely to impact my inference when comparing two models?

I am computing the WAIC (widely applicable or Watanabe-Akaike information criterion) using the waic() function from the 'loo' package in R. When I do so, I see a warning message as follows:

What should I do about this? I have noticed that several online examples/tutorials ignore this warning (e.g. here--this is a reproducible example). Is 5.1% (in my case) or 3.3% (in the example's case) a dangerously high number? How is this likely to impact my inference when comparing two models? What if the WAIC values are REALLY different in my comparison (like more than 40 WAIC units different); can I trust the inference that one model is to be preferred?

My model uses Bernoulli error, so the error term cannot be misspecified. In this class of model, the linear predictor is also unlikely to be badly misspecified.

My model uses Bernoulli error, so the error term cannot be misspecified. In my particular class of model, the linear predictor is also unlikely to be badly misspecified.