loo.varstan {bayesforecast} | R Documentation |
Leave-one-out cross-validation
Description
The loo
method for varstan objects. Computes approximate
leave-one-out cross-validation using Pareto smoothed importance
sampling (PSIS-LOO CV).
Usage
## S3 method for class 'varstan'
loo(x, ...)
Arguments
x |
A varstan object |
... |
additional values need in loo methods |
Value
an object from the loo class with the results of the Pareto-Smooth Importance Sampling, leave one out cross validation for model selection.
References
Vehtari, A., Gelman, A., & Gabry J. (2016). Practical Bayesian model
evaluation using leave-one-out cross-validation and WAIC. In Statistics
and Computing, doi:10.1007/s11222-016-9696-4
.
Gelman, A., Hwang, J., & Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing. 24, 997-1016.
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. The Journal of Machine Learning Research. 11, 3571-3594.
See Also
The loo package vignettes for demonstrations.
-
psis()
for the underlying Pareto Smoothed Importance Sampling (PSIS) procedure used in the LOO-CV approximation. -
pareto-k-diagnostic for convenience functions for looking at diagnostics.
-
loo_compare()
for model comparison.
Examples
library(astsa)
model = Sarima(birth,order = c(0,1,2),seasonal = c(1,1,1))
fit1 = varstan(model,iter = 500,chains = 1)
loo1 = loo(fit1)
loo1