# LOO NA estimates

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?

• Operating System: Linux
• brms Version: 2.10.0

You can use the newdata argument to provide the data you want to get predictions for.

Thanks, Paul.
You mean completely excluding this data point when computing loo?

``````m.int <- add_criterion(m.int, c("waic", "loo"), reloo=T, newdata=mydf[-which.min(mydf\$rt),])
``````

You asked how to exclude this data point. This is how you do it. If you can provide a minimal reproducible example I can also check if there is a bug in the code that leads to the NA value.

Thank you. This gives me the NA, tried it several times:

``````rtdata <- data.frame(rt = c(rlnorm(4999, 5.85, 0.4)+350, 300))
summary(m1 <- brm(rt ~ 1, rtdata, family=shifted_lognormal()))