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According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judges’ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" <https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> proposes a procedure for calculating a weighted average of the judges’ individual estimates such that resulting aggregate estimate appropriately combines the judges' collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate – know as "the knowledge-weighted estimate" – inputs a) judges' estimates of a continuous outcome (E) and b) predictions of others' average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data.
| Version: | 0.3.0 |
| Depends: | R (≥ 4.1) |
| Imports: | MASS, stats |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: | 2022-04-25 |
| DOI: | 10.32614/CRAN.package.metaggR |
| Author: | Ville Satopää [aut, cre, cph], Asa Palley [aut] |
| Maintainer: | Ville Satopää <ville.satopaa at gmail.com> |
| License: | GPL-2 |
| Copyright: | (c) Ville Satopaa |
| NeedsCompilation: | no |
| Citation: | metaggR citation info |
| Materials: | README NEWS |
| CRAN checks: | metaggR results |
| Reference manual: | metaggR.pdf |
| Vignettes: |
Knowledge Weighted Estimate |
| Package source: | metaggR_0.3.0.tar.gz |
| Windows binaries: | r-devel: metaggR_0.3.0.zip, r-release: metaggR_0.3.0.zip, r-oldrel: metaggR_0.3.0.zip |
| macOS binaries: | r-release (arm64): metaggR_0.3.0.tgz, r-oldrel (arm64): metaggR_0.3.0.tgz, r-release (x86_64): metaggR_0.3.0.tgz, r-oldrel (x86_64): metaggR_0.3.0.tgz |
| Old sources: | metaggR archive |
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