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Efficient approximate leave-one-out cross-validation (LOO)

Source: R/loo.R
loo.brmsfit.Rd

Perform approximate leave-one-out cross-validation based on the posterior likelihood using the loo package. For more details see loo.

Usage

# S3 method for class 'brmsfit'
loo(
  x,
  ...,
  compare = TRUE,
  resp = NULL,
  pointwise = FALSE,
  moment_match = FALSE,
  reloo = FALSE,
  k_threshold = 0.7,
  save_psis = FALSE,
  moment_match_args = list(),
  reloo_args = list(),
  model_names = NULL
)

Arguments

x

A brmsfit object.

...

More brmsfit objects or further arguments passed to the underlying post-processing functions. In particular, see prepare_predictions for further supported arguments.

compare

A flag indicating if the information criteria of the models should be compared to each other via loo_compare.

resp

Optional names of response variables. If specified, predictions are performed only for the specified response variables.

pointwise

A flag indicating whether to compute the full log-likelihood matrix at once or separately for each observation. The latter approach is usually considerably slower but requires much less working memory. Accordingly, if one runs into memory issues, pointwise = TRUE is the way to go.

moment_match

Logical; Indicate whether loo_moment_match should be applied on problematic observations. Defaults to FALSE. For most models, moment matching will only work if you have set save_pars = save_pars(all = TRUE) when fitting the model with brm. See loo_moment_match.brmsfit for more details.

reloo

Logical; Indicate whether reloo should be applied on problematic observations. Defaults to FALSE.

k_threshold

The Pareto \(k\) threshold for which observations loo_moment_match or reloo is applied if argument moment_match or reloo is TRUE. Defaults to 0.7. See pareto_k_ids for more details.

save_psis

Should the "psis" object created internally be saved in the returned object? For more details see loo.

moment_match_args

Optional named list of additional arguments passed to loo_moment_match.

reloo_args

Optional named list of additional arguments passed to reloo. This can be useful, among others, to control how many chains, iterations, etc. to use for the fitted sub-models.

model_names

If NULL (the default) will use model names derived from deparsing the call. Otherwise will use the passed values as model names.

Value

If just one object is provided, an object of class loo. If multiple objects are provided, an object of class loolist.

Details

See loo_compare for details on model comparisons. For brmsfit objects, LOO is an alias of loo. Use method add_criterion to store information criteria in the fitted model object for later usage.

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. arXiv preprint arXiv:1507.04544.

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.

Examples

# \dontrun{
# model with population-level effects only
fit1 <- brm(rating ~ treat + period + carry,
            data = inhaler)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 8e-06 seconds
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#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
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#> Chain 2: Gradient evaluation took 5e-06 seconds
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(loo1 <- loo(fit1))
#> 
#> Computed from 4000 by 572 log-likelihood matrix.
#> 
#>          Estimate   SE
#> elpd_loo   -529.5 25.9
#> p_loo         6.3  1.0
#> looic      1059.1 51.8
#> ------
#> MCSE of elpd_loo is 0.0.
#> MCSE and ESS estimates assume MCMC draws (r_eff in [0.8, 1.2]).
#> 
#> All Pareto k estimates are good (k < 0.7).
#> See help('pareto-k-diagnostic') for details.

# model with an additional varying intercept for subjects
fit2 <- brm(rating ~ treat + period + carry + (1|subject),
            data = inhaler)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 5.5e-05 seconds
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#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
(loo2 <- loo(fit2))
#> Warning: Found 3 observations with a pareto_k > 0.7 in model 'fit2'. We recommend to set 'moment_match = TRUE' in order to perform moment matching for problematic observations. 
#> 
#> Computed from 4000 by 572 log-likelihood matrix.
#> 
#>          Estimate   SE
#> elpd_loo   -522.5 26.3
#> p_loo        85.0  7.6
#> looic      1044.9 52.6
#> ------
#> MCSE of elpd_loo is NA.
#> MCSE and ESS estimates assume MCMC draws (r_eff in [0.3, 1.8]).
#> 
#> Pareto k diagnostic values:
#>                          Count Pct.    Min. ESS
#> (-Inf, 0.7]   (good)     569   99.5%   125     
#>    (0.7, 1]   (bad)        3    0.5%   <NA>    
#>    (1, Inf)   (very bad)   0    0.0%   <NA>    
#> See help('pareto-k-diagnostic') for details.

# compare both models
loo_compare(loo1, loo2)
#>  model elpd_diff se_diff p_worse diag_diff      diag_elpd
#>   fit2       0.0     0.0      NA           3 k_psis > 0.7
#>   fit1      -7.1     4.5    0.94                         
#> 
#> Diagnostic flags present.
#> See ?`loo-glossary` (sections `diag_diff` and `diag_elpd`)
#> or https://mc-stan.org/loo/reference/loo-glossary.html.
# }