See the methods in the rstanarm package for examples.
loo_linpred(object, ...)loo_epred(object, ...)
loo_predict(object, ...)
loo_predictive_interval(object, ...)
loo_pit(object, ...)
# S3 method for default
loo_pit(object, y, lw, ...)
loo_predict(), loo_epred(), loo_linpred(), and loo_pit()
(probability integral transform) methods should return a vector with
length equal to the number of observations in the data.
For discrete observations, probability integral transform is randomised to
ensure theoretical uniformity. Fix random seed for reproducible results
with discrete data. For more details, see Czado et al. (2009).
loo_predictive_interval() methods should return a two-column matrix
formatted in the same way as for predictive_interval().
The object to use.
Arguments passed to methods. See the methods in the rstanarm package for examples.
For the default method of loo_pit(), a vector of y values the
same length as the number of columns in the matrix used as object.
For the default method of loo_pit(), a matrix of log-weights of
the same length as the number of columns in the matrix used as object.
Czado, C., Gneiting, T., and Held, L. (2009). Predictive Model Assessment for Count Data. Biometrics. 65(4), 1254-1261. tools:::Rd_expr_doi("10.1111/j.1541-0420.2009.01191.x").
The rstanarm package (mc-stan.org/rstanarm) for example methods (CRAN, GitHub).
Guidelines and recommendations for developers of R packages interfacing with Stan and a demonstration getting a simple package working can be found in the vignettes included with rstantools and at mc-stan.org/rstantools/articles.