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$pointwise[,"looic"]))
[1] 2791
> which.min(mydf$rt)
[1] 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