LinearWAgg: Aggregation Method: LinearWAgg

In aggreCAT: Mathematically Aggregating Expert Judgments

Aggregation Method: LinearWAgg

Description

Calculate one of several types of linear-weighted best estimates.

Usage

LinearWAgg(
  expert_judgements,
  type = "DistLimitWAgg",
  weights = NULL,
  name = NULL,
  placeholder = FALSE,
  percent_toggle = FALSE,
  flag_loarmean = FALSE,
  round_2_filter = TRUE
)

Arguments

expert_judgements	A dataframe in the format of data_ratings.
type	One of "Judgement", "Participant", "DistLimitWAgg", "GranWAgg", or "OutWAgg".
weights	(Optional) A two column dataframe (user_name and weight) for type = "Participant" or a three two column dataframe (⁠paper_id', 'user_name⁠ and weight) for type = "Judgement"
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.
flag_loarmean	A toggle to impute log mean (defaults 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 weighted linear combinations of the best-estimate judgements for each claim.

type may be one of the following: \loadmathjax

Judgement: Weighted by user-supplied weights at the judgement level \mjdeqn\hatp_c\left( JudgementWeights \right) = \sum_i=1^N \tildew\_judgement_i,cB_i,cascii

Participant: Weighted by user-supplied weights at the participant level \mjdeqn\hatp_c\left( ParticipantWeights \right) = \sum_i=1^N \tildew\_participant_iB_i,cascii

DistLimitWAgg: Weighted by the distance of the best estimate from the closest certainty limit. Giving greater weight to best estimates that are closer to certainty limits may be beneficial. \mjdeqnw\_distLimit_i,c = \max \left(B_i,c, 1-B_i,c\right)ascii \mjdeqn\hatp_c\left( DistLimitWAgg \right) = \sum_i=1^N \tildew\_distLimit_i,cB_i,cascii

GranWAgg: Weighted by the granularity of best estimates

Individuals are weighted by whether or not their best estimates are more granular than a level of 0.05 (i.e., not a multiple of 0.05). \mjdeqnw\_gran_i = \frac1C \sum_d=1^C \left\lceil\fracB_i,d 0.05-\left\lfloor\fracB_i,d0.05\right\rfloor\right\rceil,ascii

where \mjeqn\lfloor\ \rfloorascii and \mjeqn\lceil\ \rceilascii are the mathematical floor and ceiling functions respectively. \mjdeqn\hatp_c\left( GranWAgg \right) = \sum_i=1^N \tildew\_gran_i B_i,cascii

OutWAgg: Down weighting outliers

This method down-weights outliers by using the differences from the central tendency (median) of an individual's best estimates. \mjdeqnd_i,c = \left(median{B_i,c__i=1,...,N} - B_i,c\right)^2ascii \mjdeqnw\_out_i = 1 - \fracd_i,c\max(d_c))ascii \mjdeqn\hatp_c\left( OutWAgg \right) = \sum_i=1^N \tildew\_out_iB_i,cascii

Value

A tibble of confidence scores cs for each paper_id.

Examples

LinearWAgg(data_ratings)

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