ahpsurvey: ahp.missing – R documentation

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ahp.missing

Impute missing observations using the method in Harker (1987)

Description

Imputes the missing values of a list of matrices produced by ahp.mat using the methods and assumptions made in Harker (1987). Missing values must be coded as NA. As suggested in Harker (1987), a minimum of n-1 comparisons must be made, where n is the number of attributes (assuming that the decision-maker is perfectly consistent). Note that the algorithm assumes that the NA values will be imputed under perfect consistency with the other pairwise comparisons made.

Usage

ahp.missing(ahpmat, atts, round = FALSE, limit = FALSE)

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
`round`	Rounds the imputation values of the matrix to the nearest integer if `TRUE`. Defaults to `FALSE`.
`limit`	If set to `TRUE`, if the imputation value is larger than 9 or smaller than 1/9, the value is converted to 9 and 1/9 respectively. Defaults to `FALSE`.

Value

A list of matrices with all NA values imputed.

Author(s)

Frankie Cho

References

Harker P (1987). “Incomplete pairwise comparisons in the analytic hierarchy process.” Mathematical Modelling, 9(11), 837 - 848. ISSN 0270-0255, http://www.sciencedirect.com/science/article/pii/0270025587905033.

Examples

library(magrittr)

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

data(city200)

set.seed(42)
## Make a dataframe that is missing at random
missing.df <- city200[1:10,]
for (i in 1:10){
  missing.df[i, round(stats::runif(1,1,10))] <- NA
}
missingahp <- ahp.mat(missing.df, atts, negconvert = TRUE)
ahp.missing(missingahp, atts)

ahpsurvey

Analytic Hierarchy Process for Survey Data

v0.4.1

MIT + file LICENSE

Authors

Frankie Cho [aut, cre]

Initial release