ahpsurvey (version 0.4.1)

ahp.aggpref: Aggregate priority weights

Description

Compute and aggregate individual priority weights from pairwise comparison matrices

Usage

ahp.aggpref(ahpmat, atts, method = "geometric", aggmethod = method, 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

method

if method = "eigen", the individual priority weights are computed using the Dominant Eigenvalues method described in Saaty2003;textualahpsurvey. Otherwise, then the priorities are computed based on the averages of normalized values. Basically it normalizes the matrices so that all of the columns add up to 1, and then computes the averages of the row as the priority weights of each attribute. Three modes of finding the averages are available: arithmetic: the arithmetic mean; geometric: the geometric mean (the default); rootmean: the square root of the sum of the squared value.

aggmethod

how to aggregate the individual priorities. By default aggmethod = method. Apart from the methods offered in method, aggmethod also permits three other options: tmean computes the trimmed arithmetic mean, tgmean computes the trimmed geometric mean (both with quantiles trimmed based on qt), and sd computes the standard deviation from the arithmetic mean. If method = "eigen" and aggmethod is not specified, aggmethod defaults to "geometric".

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 priorities of all the decision-makers.

References

Examples

# NOT RUN {
## Computes individual priorities with geometric mean and aggregates them
## with a trimmed arithmetic mean

library(magrittr)

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

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

# }