bayesforecast (version 1.0.1)

loo.varstan: 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 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

# NOT RUN {
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
# }
# NOT RUN {
# }