loo_bgam: Calls the 'loo' package to perform efficient approximate...

In bayesGAM: Fit Multivariate Response Generalized Additive Models using Hamiltonian Monte Carlo

Calls the loo package to perform efficient approximate leave-one-out cross-validation on models fit with bayesGAM

Description

Computes PSIS-LOO CV, efficient approximate leave-one-out (LOO) cross-validation for Bayesian models using Pareto smoothed importance sampling (PSIS). This calls the implementation from the loo package of the methods described in Vehtari, Gelman, and Gabry (2017a, 2017b).

Usage

loo_bgam(object, ...)

## S4 method for signature 'bayesGAMfit'
loo_bgam(object, ...)

## S4 method for signature 'array'
loo_bgam(object, ...)

Arguments

object	Object of type bayesGAMfit generated from bayesGAM.
...	Additional parameters to pass to pass to loo::loo

Value

a named list of class c("psis_loo", "loo")

estimates

A matrix with two columns (Estimate, SE) and three rows (elpd_loo, p_loo, looic). This contains point estimates and standard errors of the expected log pointwise predictive density (elpd_loo), the effective number of parameters (p_loo) and the LOO information criterion looic (which is just -2 * elpd_loo, i.e., converted to deviance scale).

pointwise

A matrix with five columns (and number of rows equal to the number of observations) containing the pointwise contributions of the measures (elpd_loo, mcse_elpd_loo, p_loo, looic, influence_pareto_k). in addition to the three measures in estimates, we also report pointwise values of the Monte Carlo standard error of elpd_loo (mcse_elpd_loo), and statistics describing the influence of each observation on the posterior distribution (influence_pareto_k). These are the estimates of the shape parameter k of the generalized Pareto fit to the importance ratios for each leave-one-out distribution. See the pareto-k-diagnostic page for details.

diagnostics

A named list containing two vectors:

• pareto_k: Importance sampling reliability diagnostics. By default, these are equal to the influence_pareto_k in pointwise. Some algorithms can improve importance sampling reliability and modify these diagnostics. See the pareto-k-diagnostic page for details.

• n_eff: PSIS effective sample size estimates.

psis_object

This component will be NULL unless the save_psis argument is set to TRUE when calling loo(). In that case psis_object will be the object of class "psis" that is created when the loo() function calls psis() internally to do the PSIS procedure.

References

Vehtari, A., Gelman, A., and Gabry, J. (2017a). Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Statistics and Computing. 27(5), 1413–1432. doi:10.1007/s11222-016-9696-4 (journal version, preprint arXiv:1507.04544).

Vehtari, A., Gelman, A., and Gabry, J. (2017b). Pareto smoothed importance sampling. preprint arXiv:1507.02646

Vehtari A, Gabry J, Magnusson M, Yao Y, Gelman A (2019). “loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models.” R package version 2.2.0, <URL: https://mc-stan.org/loo>.

Examples

f <- bayesGAM(weight ~ np(height), data = women,
              family = gaussian, iter=500, chains = 1)
loo_bgam(f)

bayesGAM documentation built on March 18, 2022, 6:29 p.m.