Description Usage Arguments Value References Examples
This is used to evaluate the fit of the model using the Watanabe-Akaike Information criteria
1 | get_waic(bpc_object)
|
bpc_object |
a bpc object |
a loo object
Gelman, Andrew, Jessica Hwang, and Aki Vehtari. Understanding predictive information criteria for Bayesian models. Statistics and computing 24.6 (2014): 997-1016.
1 2 3 4 5 6 7 8 | m<-bpc(data = tennis_agresti,
player0 = 'player0',
player1 = 'player1',
result_column = 'y',
model_type = 'bt',
solve_ties = 'none')
waic<-get_waic(m)
print(waic)
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