Analytic Hierarchy Process for Survey Data

The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987) that allows decision-makers to weigh attributes and evaluate alternatives presented to them. This package provides a consistent methodology for researchers to reformat data and run analytic hierarchy process in R on data that are formatted using the survey data entry mode. It is optimized for performing the analytic hierarchy process with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and test multiple options. It also allows researchers to quantify, visualize and correct for inconsistency in the decision-maker's comparisons.

Overview

The ahpsurvey package provides a consistent methodology for researchers to reformat data and run the analytic hierarchy process (AHP), introduced by Thomas Saaty, on data that are formatted with the survey data entry mode. It is optimised for performing the AHP with many decision-makers, and provides tools and options for researchers to aggregate individual preferences and concurrently test multiple aggregation options. It also allows researchers to quantify, visualise and correct for inconsistent pairwise comparisons.

Installation

Install ahpsurvey directly from CRAN:

install.packages("ahpsurvey",repos = "http://cran.us.r-project.org")
#> Installing package into '/Users/chohinting/Library/R/3.5/library'
#> (as 'lib' is unspecified)
#> installing the source package 'ahpsurvey'

Or, install the development version of ahpsurvey from Github with:

# install.packages("devtools")
devtools::install_github("frankiecho/ahpsurvey")

Usage

Here, we have the simulated survey data of pairwise comparisons of 200 decision-makers who responded to a survey about the attributes they think is important when choosing a city to live in.

library(ahpsurvey)
library(magrittr)
 
data(city200)
city200 %>% head()
#>   cult_fam cult_house cult_jobs cult_trans fam_house fam_jobs fam_trans
#> 1        2         -2         2         -6        -4       -4        -8
#> 2        2         -4         1         -4        -4       -2        -8
#> 3        4         -2         1         -3        -7       -3        -5
#> 4        8         -4         3         -4        -8        1        -7
#> 5        3         -3         5         -6        -8        1        -4
#> 6        6         -4         2         -4        -7       -2        -4
#>   house_jobs house_trans jobs_trans
#> 1          4          -3         -8
#> 2          4          -3         -7
#> 3          4          -3         -6
#> 4          4          -3         -9
#> 5          4          -3         -6
#> 6          4          -3         -6

ahpsurvey allows us to convert this data.frame into a usable list of pairwise comparison matrices for our further action:

## Define the attributes used
atts <- c("cult", "fam", "house", "jobs", "trans")
 
city200 %>%
  ahp.mat(atts = atts, negconvert = TRUE) %>%
  head(2)
#> [[1]]
#>            cult   fam     house  jobs trans
#> cult  1.0000000 0.500 2.0000000 0.500     6
#> fam   2.0000000 1.000 4.0000000 4.000     8
#> house 0.5000000 0.250 1.0000000 0.250     3
#> jobs  2.0000000 0.250 4.0000000 1.000     8
#> trans 0.1666667 0.125 0.3333333 0.125     1
#> 
#> [[2]]
#>       cult   fam     house      jobs trans
#> cult  1.00 0.500 4.0000000 1.0000000     4
#> fam   2.00 1.000 4.0000000 2.0000000     8
#> house 0.25 0.250 1.0000000 0.2500000     3
#> jobs  1.00 0.500 4.0000000 1.0000000     7
#> trans 0.25 0.125 0.3333333 0.1428571     1

And can calculate the aggregated individual preferences of the 200 decision-makers using that list:

city200 %>%
  ahp.mat(atts = atts, negconvert = TRUE) %>%
  ahp.aggpref(atts = atts)
#>       cult        fam      house       jobs      trans 
#> 0.15265560 0.44012225 0.07217919 0.28341600 0.03911341

Further arguments allow you to specify the aggregation method, impute missing values and identify and correct inconsistent responses.

Functions

An overview of the functions in this package are as follows:

• ahp.mat: Generate AHP pairwise matrices from survey data
• ahp.indpref: Priority weights of individual decision-makers
• ahp.aggpref: Aggregate individual priorities (AIP)
• ahp.aggjudge: Aggregate individual judgements (AIJ)
• ahp.cr: Saaty’s Consistency Ratio
• ahp.error: The product between the pairwise comparison value and pj/pi
• ahp.pwerror: Finds the pairwise comparisons with the maximum amount of inconsistency
• ahp.missing: Impute missing pairwise comparsions
• ahp.harker: Replace inconsistent pairwise comparisons

Vignettes

For a detailed example of how the above function works, look no further than the vignettes, which are stored in /my-vignette.pdf. There, you can find a detailed step-by-step instruction of how to use the function using a simulated survey dataset and visualise the output using ggplot2.

Future development

I have plans to add the following features in the future, perhaps after I finish writing up my masters thesis :-(

• Multiple level of attributes: right now, you can always multiply the weights manually, but I’m looking to develop this feature in a convenient function
• Comparing alternatives: or a way to export the matrices to be used in other packages which does this
• Sensitivity analysis
• More ways to impute missing data
• Fuzzy AHP (or integration with existing packages)

Please let me know if there are any features which could be useful to you in a feature request or contribution.

Author

• Frankie Cho - Author & Maintainer - frankiecho

License

This project is licensed under the MIT License.