loo.varstan | R Documentation |
The loo
method for varstan
objects. Computes approximate
leave-one-out cross-validation using Pareto smoothed importance
sampling (PSIS-LOO CV).
## S3 method for class 'varstan'
loo(x, ...)
x |
A |
... |
additional values need in loo methods |
an object from the loo class with the results of the Pareto-Smooth Importance Sampling, leave one out cross validation for model selection.
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.
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.
model = Sarima(birth,order = c(0,1,2),seasonal = c(1,1,1))
fit1 = varstan(model,iter = 500,chains = 1)
loo1 = loo(fit1)
loo1
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