loo.moultmcmc: Efficient approximate leave-one-out cross-validation (LOO)

In pboesu/moultmcmc: Bayesian Inference for Avian Moult Phenology Models

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

Description

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

Usage

## S3 method for class 'moultmcmc'
loo(x, cores = getOption("mc.cores", 1))

Arguments

x

A moultmcmc model object

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).

Value

The loo() methods return a named list with class c("psis_loo", "loo"). See ?loo::loo

pboesu/moultmcmc documentation built on Feb. 19, 2023, 6:03 a.m.