Aggregation Method: AverageWAgg — AverageWAgg • aggreCAT

Calculate one of several types of averaged best estimates.

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

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.

Value

A tibble of confidence scores cs for each paper_id.

Details

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

type may be one of the following:

ArMean: Arithmetic mean of the best estimates \[\hat{p}_c\left(ArMean \right ) = \frac{1}{N}\sum_{i=1}^N B_{i,c}\] Median: Median of the best estimates \[\hat{p}_c \left(\text{median} \right) = \text{median} { B^i_c}_{i=1,...,N}\] GeoMean: Geometric mean of the best estimates \[GeoMean_{c}= \left(\prod_{i=1}^N B_{i,c}\right)^{\frac{1}{N}}\] LOArMean: Arithmetic mean of the log odds transformed best estimates \[LogOdds_{i,c}= \frac{1}{N} \sum_{i=1}^N log\left( \frac{B_{i,c}}{1-B_{i,c}}\right)\] The average log odds estimate is then back transformed to give a final group estimate: \[\hat{p}_c\left( LOArMean \right) = \frac{e^{LogOdds_{i,c}}}{1+e^{LogOdds_{i,c}}}\] ProbitArMean: Arithmetic mean of the probit transformed best estimates \[Probit_{c}= \frac{1}{N} \sum_{i=1}^N \Phi^{-1}\left( B_{i,c}\right)\] The average probit estimate is then back transformed to give a final group estimate: \[\hat{p}_c\left(ProbitArMean \right) = \Phi\left({Probit_{c}}\right)\]

Examples

if (FALSE) AverageWAgg(data_ratings)