Leave one group out cross-validation for baggr models
Performs exact leave-one-group-out cross-validation on a baggr model.
loocv(data, return_models = FALSE, ...)
The values returned by loocv() can be used to understand how any
one group affects the overall result, as well as how well the model
predicts the omitted group.
This function automatically runs K baggr models, leaving out one group at a time, and then calculates expected log predictive density (ELPD) for that group (see Gelman et al 2013). The main output is the cross-validation information criterion, or -2 times the ELPD averaged over 'K' models. This is related to, and often approximated by, the Watanabe-Akaike Information Criterion. A value closer to zero (i.e. a smaller number in magnitude) means a better fit. For more information on cross-validation see this overview article
For running more computation-intensive models, consider setting the
mc.cores option before running loocv, e.g. options(mc.cores = 4)
(by default baggr runs 4 MCMC chains in parallel).
As a default, rstan runs "silently" (refresh=0).
To see sampling progress, please set e.g. loocv(data, refresh = 500).
log predictive density value, an object of class baggr_cv;
full model, prior values and lpd of each model are also returned.
These can be examined by using attributes() function.
Witold Wiecek
Gelman, Andrew, Jessica Hwang, and Aki Vehtari. “Understanding Predictive Information Criteria for Bayesian Models.” Statistics and Computing 24, no. 6 (November 2014): 997–1016. https://doi.org/10.1007/s11222-013-9416-2.
# even simple examples may take a while cv <- loocv(schools, pooling = "partial") print(cv) # returns the lpd value attributes(cv) # more information is included in the object
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