Estimates the leave-future-out (LFO) information criterion for dynamite
models using Pareto smoothed importance sampling.
Arguments
- x
[
dynamitefit
]
The model fit object.- L
[
integer(1)
]
Positive integer defining how many time points should be used for the initial fit.- verbose
[
logical(1)
]
IfTRUE
(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.- ...
Additional arguments passed to
rstan::sampling()
orcmdstanr::sample()
, such aschains
andcores
(parallel_chains
incmdstanr
).
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.
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.
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
loo.dynamitefit()
,
mcmc_diagnostics()
Examples
# \donttest{
# Please update your rstan and StanHeaders installation before running
# on Windows
if (!identical(.Platform$OS.type, "windows")) {
# this gives warnings due to the small number of iterations
out <- suppressWarnings(lfo(gaussian_example_fit, L = 20))
out$ELPD
out$ELPD_SE
}
#> Estimating model with 20 time points.
#> Estimating model with 22 time points.
#> Estimating model with 24 time points.
#> Estimating model with 27 time points.
#> [1] 17.31789
# }