AverageWAgg {aggreCAT} | R Documentation |
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 |
name |
Name for aggregation method. Defaults to |
placeholder |
Toggle the output of the aggregation method to impute placeholder data. |
percent_toggle |
Change the values to probabilities. Default is |
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:
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: \[\hat{p}_c\left(ProbitArMean \right) = \Phi\left({Probit_{c}}\right)\]
Value
A tibble of confidence scores cs
for each paper_id
.
Examples
AverageWAgg(data_ratings)