I fitted a shifted lognormal model to response times. Adding WAIC and LOO to this model works, albeit with one (or more) datapoints with a high Pareto k.
However, refitting the model without the problematic data points leads to NaN/NA estimates for LOOIC, etc.
m.int <- add_criterion(m.int, c("waic", "loo"), reloo=T) loo(m.int) Computed from 15000 by 6512 log-likelihood matrix Estimate SE elpd_loo NaN NA p_loo NaN NA looic NaN NA
Checking the pointwise values showed that that one particular data point was NA, and it turned out to be the fastest RT. (this was also the datapoint that had a very high Pareto k without reloo).
> which(is.na(loo(m.int$pointwise[,"looic"]))  2791 > which.min(mydf$rt)  2791
Perhaps this has something to do with the ndt parameter of the shifted lognormal?
How can I obtain a pointwise value for the model without this datapoint, and thus a LOOIC for the model?
Please also provide the following information in addition to your question:
- Operating System: Linux
- brms Version: 2.10.0