Pareto.pdf (5.6 KB) Short summary of the problem

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How to remove observations with a pareto_k > 0.7
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If possible, add also code to simulate data or attach a (subset of) the dataset you work with.

Please also provide the following information in addition to your question:

- Operating System: macOS
- brms Version:

• 2.13.0

I used an approximate leave-one-out cross-validation to validate a model and got this warning message.

*“Found 3 observations with a pareto_k > 0.7 in model ‘SEM_brms’. It is recommended to set ‘reloo = TRUE’ to calculate the ELPD without the assumption that these observations are negligible. This will refit the model 3 times to compute the ELPDs for the problematic observations directly.”*

I then tried to find out the observations that have pareto_k > 0.7. I plotted the loo plot with label equal=TRUE: plot (Criteria_pop$criteria$loo, label_points = TRUE) and plotted the figure attached above (Pareto.pdf). From that figure, it is clear that the observations 23, 34, and 46 are “influential” data points.

My questions is the following.

**Is there an automatic way to delete these data? By removing these “influential” data points, I expect to improve the model and estimate the new posterior and see if they differ from the first one, I got with the “influential” data points.**

Thanks in advance