R: Convenience function for computing relative efficiencies

relative_eff {loo}

R Documentation

Convenience function for computing relative efficiencies

Description

relative_eff computes the the MCMC effective sample size divided by the total sample size.

Usage

relative_eff(x, ...)

## Default S3 method:
relative_eff(x, chain_id, ...)

## S3 method for class 'matrix'
relative_eff(x, chain_id, ..., cores = getOption("mc.cores",
  1))

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

## S3 method for class 'function'
relative_eff(x, chain_id, ...,
  cores = getOption("mc.cores", 1), data = NULL, draws = NULL)

Arguments

`x`	A vector, matrix, 3-D array, or function. See the Methods (by class) section below for details on the shape of `x`. For use with the `loo` function, the values in `x` (or generated by `x` if `x` is a function) should be likelihood values (i.e., `exp(log_lik)`, not on the log scale). For generic `use` with `psis`, the values in `x` should be the reciprocal of the importance ratios (i.e., `exp(-log_ratios)`).
`chain_id`	A vector of length `NROW(x)` containing MCMC chain indexes for each each row of `x` (if a matrix) or each value in `x` (if a vector). No `chain_id` is needed if `x` is a 3-D array. If there are `C` chains then valid chain indexes are values in `1:C`.
`cores`	The number of cores to use for parallelization.
`data, draws, ...`	Same as for the `loo` function method.

Value

A vector of relative effective sample sizes.

Methods (by class)

default: A vector of length S (posterior sample size).
matrix: An S by N matrix, where S is the size of the posterior sample (with all chains merged) and N is the number of data points.
array: An I by C by N array, where I is the number of MCMC iterations per chain, C is the number of chains, and N is the number of data points.
function: A function f that takes arguments data_i and draws and returns a vector containing the log-likelihood for a single observation i evaluated at each posterior draw. The function should be written such that, for each observation i in 1:N, evaluating f(data_i = data[i,, drop=FALSE], draws = draws) results in a vector of length S (size of posterior sample). The log-likelihood function can also have additional arguments but data_i and draws are required.

If using the function method then the arguments data and draws must also be specified in the call to loo:
- data: A data frame or matrix containing the data (e.g. observed outcome and predictors) needed to compute the pointwise log-likelihood. For each observation i, the ith row of data will be passed to the data_i argument of the log-likelihood function.
- draws: An object containing the posterior draws for any parameters needed to compute the pointwise log-likelihood. Unlike data, which is indexed by observation, for each observation the entire object draws will be passed to the draws argument of the log-likelihood function.
- The ... can be used to pass additional arguments to your log-likelihood function. These arguments are used like the draws argument in that they are recycled for each observation.

Examples

LLarr <- example_loglik_array()
LLmat <- example_loglik_matrix()
dim(LLarr)
dim(LLmat)

rel_n_eff_1 <- relative_eff(exp(LLarr))
rel_n_eff_2 <- relative_eff(exp(LLmat), chain_id = rep(1:2, each = 500))
all.equal(rel_n_eff_1, rel_n_eff_2)

[Package loo version 2.0.0 Index]