weight_outlier: Weighting method: Down weighting outliers

In aggreCAT: Mathematically Aggregating Expert Judgments

Weighting method: Down weighting outliers

Description

This method down-weights outliers.

Usage

weight_outlier(expert_judgements)

Arguments

expert_judgements

A dataframe in the form of data_ratings

Details

This function is used by LinearWAgg to weighting functions for the aggregation type "OutWAgg". Outliers are given less weight by using the squared difference between the median of an individual's best estimates across all claims and their best estimate for the claim being assessed: \loadmathjax \mjdeqnd_i,c = \left(median{B_i,c__i=1,...,N} - B_i,c\right)^2ascii

Weights are given by 1 minus the proportion of the individual's squared difference relative to the maximum squared difference for the claim across all individuals:

\mjdeqn

w\_out_i = 1 - \fracd_i,c\max(d_c))ascii

Value

A tibble in the form of the input expert_judgements argument with additional columns supplying the calculated weight for each row's observation.

See Also

Other weighting functions: weight_asym(), weight_interval(), weight_nIndivInterval(), weight_reason(), weight_reason2(), weight_varIndivInterval()

aggreCAT documentation built on May 4, 2026, 5:07 p.m.