fangs (version 0.2.21)

compute_expected_loss: Estimate the expected FARO Loss for a Feature Allocation

Description

A Monte Carlo estimate of the expected FARO loss is computed for a feature allocation given a set of posterior samples.

Usage

compute_expected_loss(samples, Z, a = 1, nCores = 0)

Value

The estimated expected FARO loss as a scalar value.

Arguments

samples

An object of class ‘list’ containing posterior samples from a feature allocation distribution. Each list element encodes one feature allocation as a binary matrix, with items in the rows and features in the columns.

Z

A feature allocation in binary matrix form, with items in the rows and features in the columns.

a

A numeric scalar for the cost parameter of generalized Hamming distance used in FARO loss. The other cost parameter, \(b\), is equal to \(2 - a\).

nCores

The number of CPU cores to use, i.e., the number of simultaneous calculations at any given time. A value of zero indicates to use all cores on the system.

References

D. B. Dahl, D. J. Johnson, R. J. Andros (2023), Comparison and Bayesian Estimation of Feature Allocations, Journal of Computational and Graphical Statistics, tools:::Rd_expr_doi("10.1080/10618600.2023.2204136").

Examples

data(samplesFA)
Z <- matrix(sample(c(0,1), 60, replace=TRUE), byrow=TRUE, nrow=20)
compute_expected_loss(samplesFA, Z)