ahp.aggjudge: Aggregate individual judgments
In ahpsurvey: Analytic Hierarchy Process for Survey Data

Description Usage Arguments Value Author(s) Examples

Aggregate individual judgments from pairwise comparison matrices

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

`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`.

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

Frankie Cho

## 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)