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Currently, brms drops the censored data for most pp_check types. A better approach would be to impute the observations. See a bayesplot issue stan-dev/bayesplot#319 for a proof-of-concept and example plots. The imputation part would be natural to be in brms and bayesplot would just support a different color for imputed observations.
The text was updated successfully, but these errors were encountered:
The imputation part requires sampling from a truncated posterior predictive distribution. In many cases rejection sampling is likely to be fast enough, and simplest to implement, but still requires some checks.
This seems to be related to my proposal in #1657. With the data augmentation / imputation approach for modelling censored data, the censored data would be imputed during model fitting and could then probably be used in posterior predictive checks?
Currently,
brms
drops the censored data for mostpp_check
types. A better approach would be to impute the observations. See abayesplot
issue stan-dev/bayesplot#319 for a proof-of-concept and example plots. The imputation part would be natural to be inbrms
andbayesplot
would just support a different color for imputed observations.The text was updated successfully, but these errors were encountered: