Package: metaggR 0.3.0

metaggR: Calculate the Knowledge-Weighted Estimate

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.

Authors:Ville Satopää [aut, cre, cph], Asa Palley [aut]

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NEWS

# Install 'metaggR' in R:
install.packages('metaggR', repos = c('https://satopaa.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.85 score 1 stars 14 scripts 208 downloads 4 exports 1 dependencies

Last updated 3 years agofrom:6aec9ebb60. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-winOKNov 08 2024
R-4.5-linuxOKNov 08 2024
R-4.4-winOKNov 08 2024
R-4.4-macOKNov 08 2024
R-4.3-winOKNov 08 2024
R-4.3-macOKNov 08 2024

Exports:get_influence_scoresknowledge_gapknowledge_weighted_estimateknowledge_weights

Dependencies:MASS

Knowledge Weighted Estimate

Rendered fromuser_manual.Rmdusingknitr::rmarkdownon Nov 08 2024.

Last update: 2021-08-17
Started: 2021-01-09

Readme and manuals

Help Manual

Help pageTopics
Data: Calorie CountsCalorie_Counts E_CALORIES_FINAL E_CALORIES_INITIAL ID_CALORIES P_CALORIES THETA_CALORIES
Data: Coin FlipsCoin_Flips E_COINS_NESTED E_COINS_NESTED_SYMMETRIC E_COINS_SYMMETRIC ID_COINS_NESTED ID_COINS_NESTED_SYMMETRIC ID_COINS_SYMMETRIC P_COINS_NESTED P_COINS_NESTED_SYMMETRIC P_COINS_SYMMETRIC THETA_COINS_NESTED THETA_COINS_NESTED_SYMMETRIC THETA_COINS_SYMMETRIC
Data: General Knowledge StatementsE_GK_1 E_GK_2 E_GK_3 E_GK_4 E_GK_5 General_Knowledge_Statements ID_GK_1 ID_GK_2 ID_GK_3 ID_GK_4 ID_GK_5 P_GK_1 P_GK_2 P_GK_3 P_GK_4 P_GK_5 THETA_GK_1 THETA_GK_2 THETA_GK_3 THETA_GK_4 THETA_GK_5
Calculate the Influence Scoresget_influence_scores
Data: Grocery PricesE_GROCERIES Grocery_Prices ID_GROCERIES P_GROCERIES THETA_GROCERIES
Calculate the Knowledge Gapknowledge_gap
Knowledge-Weighted Estimateknowledge_weighted_estimate
Calculate the Weights that Minimize the Knowledge Gapknowledge_weights
Data: NCAA BasketballE_NCAA_R16 E_NCAA_R64 ID_NCAA_R16 ID_NCAA_R64 NCAA_Basketball P_NCAA_R16 P_NCAA_R64 THETA_NCAA_R16 THETA_NCAA_R64