AverageWAgg: Aggregation Method: AverageWAgg

In aggreCAT: Mathematically Aggregating Expert Judgments

Aggregation Method: AverageWAgg

Description

Calculate one of several types of averaged best estimates.

Usage

AverageWAgg(
  expert_judgements,
  type = "ArMean",
  name = NULL,
  placeholder = FALSE,
  percent_toggle = FALSE,
  round_2_filter = TRUE
)

Arguments

expert_judgements	A dataframe in the format of data_ratings.
type	One of "ArMean", "Median", "GeoMean", "LOArMean", or "ProbitArMean".
name	Name for aggregation method. Defaults to type unless specified.
placeholder	Toggle the output of the aggregation method to impute placeholder data.
percent_toggle	Change the values to probabilities. Default is FALSE.
round_2_filter	Note that the IDEA protocol results in both a Round 1 and Round 2 set of probabilities for each claim. Unless otherwise specified, we will assume that the final Round 2 responses (after discussion) are being referred to.

Details

This function returns the average, median and transformed averages of best-estimate judgements for each claim.

type may be one of the following: \loadmathjax

ArMean: Arithmetic mean of the best estimates \mjdeqn\hatp_c\left(ArMean \right ) = \frac1N\sum_i=1^N B_i,cascii Median: Median of the best estimates \mjdeqn\hatp_c \left(\textmedian \right) = \textmedian { B^i_c}_i=1,...,Nascii GeoMean: Geometric mean of the best estimates \mjdeqnGeoMean_c= \left(\prod_i=1^N B_i,c\right)^\frac1Nascii LOArMean: Arithmetic mean of the log odds transformed best estimates \mjdeqnLogOdds_i,c= \frac1N \sum_i=1^N log\left( \fracB_i,c1-B_i,c\right)ascii The average log odds estimate is then back transformed to give a final group estimate: \mjdeqn\hatp_c\left( LOArMean \right) = \frace^LogOdds_i,c1+e^LogOdds_i,cascii ProbitArMean: Arithmetic mean of the probit transformed best estimates \mjdeqnProbit_c= \frac1N \sum_i=1^N \Phi^-1\left( B_i,c\right)ascii The average probit estimate is then back transformed to give a final group estimate: \mjdeqn\hatp_c\left(ProbitArMean \right) = \Phi\left(Probit_c\right)ascii

Value

A tibble of confidence scores cs for each paper_id.

Examples

AverageWAgg(data_ratings)

aggreCAT documentation built on June 8, 2025, 11:06 a.m.