get_waic: Tiny wrapper for the WAIC method from the loo package.
In bpcs: Bayesian Paired Comparison Analysis with Stan
Description
Usage
Arguments
Value
References
Examples
Description
This is used to evaluate the fit of the model using the Watanabe-Akaike Information criteria
Usage
1
get_waic(bpc_object)
Arguments
bpc_object
a bpc object
Value
a loo object
References
Gelman, Andrew, Jessica Hwang, and Aki Vehtari. Understanding predictive information criteria for Bayesian models. Statistics and computing 24.6 (2014): 997-1016.
Examples
1
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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)
bpcs documentation built on Dec. 15, 2020, 5:23 p.m.
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Description Usage Arguments Value References Examples
Description
This is used to evaluate the fit of the model using the Watanabe-Akaike Information criteria
Usage
1 | get_waic(bpc_object)
|
Arguments
bpc_object |
a bpc object |
Value
a loo object
References
Gelman, Andrew, Jessica Hwang, and Aki Vehtari. Understanding predictive information criteria for Bayesian models. Statistics and computing 24.6 (2014): 997-1016.
Examples
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)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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