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ahp.aggjudge

Aggregate individual judgments

Description

Aggregate individual judgments from pairwise comparison matrices

Usage

ahp.aggjudge(ahpmat, atts, aggmethod = "geometric", qt = 0)

Arguments

ahpmat

A list of pairwise comparison matrices of each decision maker generated by ahp.mat.
atts

a list of attributes in the correct order
aggmethod

The method of aggregating the judgments by all decision-makers. Five modes aggregation are available: arithmetic: the arithmetic mean; geometric: the geometric mean (the default); rootmean: the square root of the sum of the squared value, tmean: the trimmed mean, tgmean: trimmed geometric mean. The quantiles trimmed are based on qt. It can also be set to sd, where it reports the standard deviation from the arithmetic mean.
qt

specifies the quantile which the top and bottom priority weights are trimmed. Used only if aggmethod = 'tmean' or aggmethod = 'tgmean'. For example, qt = 0.25 specifies that the aggregation is the arithmetic mean of the values from the 25 to 75 percentile. By default qt = 0.

Value

A data.frame of the aggregated pairwise judgments of all the decision-makers.

Author(s)

Frankie Cho

Examples

## Computes individual judgments with geometric mean and aggregates them
## with a trimmed arithmetic mean

data(city200)
atts <- c('cult', 'fam', 'house', 'jobs', 'trans')

cityahp <- ahp.mat(df = city200, atts = atts, negconvert = TRUE)
ahp.aggjudge(cityahp, atts, aggmethod = 'tmean', qt = 0.1)
ahpsurvey

Analytic Hierarchy Process for Survey Data

v0.4.1
MIT + file LICENSE
Authors
Frankie Cho [aut, cre]
Initial release

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