loo: loo_moment_match_split – R documentation

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loo_moment_match_split

Split moment matching for efficient approximate leave-one-out cross-validation (LOO)

Description

A function that computes the split moment matching importance sampling loo. Takes in the moment matching total transformation, transforms only half of the draws, and computes a single elpd using multiple importance sampling.

Usage

loo_moment_match_split(
  x,
  upars,
  cov,
  total_shift,
  total_scaling,
  total_mapping,
  i,
  log_prob_upars,
  log_lik_i_upars,
  r_eff_i,
  cores,
  is_method,
  ...
)

Arguments

`x`	A fitted model object.
`upars`	A matrix containing the model parameters in unconstrained space where they can have any real value.
`cov`	Logical; Indicate whether to match the covariance matrix of the samples or not. If `FALSE`, only the mean and marginal variances are matched.
`total_shift`	A vector representing the total shift made by the moment matching algorithm.
`total_scaling`	A vector representing the total scaling of marginal variance made by the moment matching algorithm.
`total_mapping`	A vector representing the total covariance transformation made by the moment matching algorithm.
`i`	Observation index.
`log_prob_upars`	A function that takes arguments `x` and `upars` and returns a matrix of log-posterior density values of the unconstrained posterior draws passed via `upars`.
`log_lik_i_upars`	A function that takes arguments `x`, `upars`, and `i` and returns a vector of log-likeliood draws of the `i`th observation based on the unconstrained posterior draws passed via `upars`.
`r_eff_i`	MCMC relative effective sample size of the `i`'th log likelihood draws.
`cores`	The number of cores to use for parallelization. This defaults to the option `mc.cores` which can be set for an entire R session by `options(mc.cores = NUMBER)`. The old option `loo.cores` is now deprecated but will be given precedence over `mc.cores` until `loo.cores` is removed in a future release. As of version 2.0.0 the default is now 1 core if `mc.cores` is not set, but we recommend using as many (or close to as many) cores as possible. Note for Windows 10 users: it is strongly recommended to avoid using the `.Rprofile` file to set `mc.cores` (using the `cores` argument or setting `mc.cores` interactively or in a script is fine).
`is_method`	The importance sampling method to use. The following methods are implemented: `"psis"`: Pareto-Smoothed Importance Sampling (PSIS). Default method. `"tis"`: Truncated Importance Sampling (TIS) with truncation at `sqrt(S)`, where `S` is the number of posterior draws. `"sis"`: Standard Importance Sampling (SIS).
`...`	Further arguments passed to the custom functions documented above.

Value

A list containing the updated log-importance weights and log-likelihood values. Also returns the updated MCMC effective sample size and the integrand-specific log-importance weights.

References

Paananen, T., Piironen, J., Buerkner, P.-C., Vehtari, A. (2020). Implicitly Adaptive Importance Sampling. preprint arXiv:1906.08850