ahp.indpref {ahpsurvey}

Computes priority weights of individual decision-makers

Description

ahp.indpref computes the individual priorities of the decision-makers, and returns a data.frame containing the priority weights of the decision-makers.

Usage

ahp.indpref(ahpmat, atts, method = "geometric")

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 Saaty (2003). 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 other 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.

Value

A data.frame of the individual priorities of all the decision-makers.

Author(s)

Frankie Cho

References

Saaty TL (2003). “Decision-making with the AHP: Why is the principal eigenvector necessary.” European Journal of Operational Research, 145(1), 85 - 91. ISSN 0377-2217, http://www.sciencedirect.com/science/article/pii/S0377221702002278.

Examples

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

cityahp <- ahp.mat(df = city200, atts = atts, negconvert = TRUE) 
ahp.indpref(cityahp, atts, method = "eigen")