lfo {dynamite}

Approximate Leave-Future-Out (LFO) Cross-validation

Description

Estimates the leave-future-out (LFO) information criterion for dynamite models using Pareto smoothed importance sampling.

Usage

lfo(x, ...)

## S3 method for class 'dynamitefit'
lfo(x, L, verbose = TRUE, k_threshold = 0.7, ...)

Arguments

x

[dynamitefit]
The model fit object.

...

Additional arguments passed to rstan::sampling() or cmdstanr::sample(), such as chains and cores (parallel_chains in cmdstanr).

L

[integer(1)]
Positive integer defining how many time points should be used for the initial fit.

verbose

[logical(1)]
If TRUE (default), print the progress of the LFO computations to the console.

k_threshold

[numeric(1)]
Threshold for the Pareto k estimate triggering refit. Default is 0.7.

Details

For multichannel models, the log-likelihoods of all channels are combined. For models with groups, expected log predictive densities (ELPDs) are computed independently for each group, but the re-estimation of the model is triggered if pareto k values of any group exceeds the threshold.

Value

An lfo object which is a list with the following components:

• ELPD
  Expected log predictive density estimate.

• ELPD_SE
  Standard error of ELPD. This is a crude approximation which does not take into account potential serial correlations.

• pareto_k
  Pareto k values.

• refits
  Time points where model was re-estimated.

• L
  L value used in the LFO estimation.

• k_threshold
  Threshold used in the LFO estimation.

References

Paul-Christian Bürkner, Jonah Gabry, and Aki Vehtari (2020). Approximate leave-future-out cross-validation for Bayesian time series models, Journal of Statistical Computation and Simulation, 90:14, 2499-2523.

See Also

Model diagnostics hmc_diagnostics(), loo.dynamitefit(), mcmc_diagnostics()

Examples

data.table::setDTthreads(1) # For CRAN

# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
  # this gives warnings due to the small number of iterations
  out <- suppressWarnings(
    lfo(gaussian_example_fit, L = 20, chains = 1, cores = 1)
  )
  out$ELPD
  out$ELPD_SE
  plot(out)
}