The Analytic Hierarchy Process is a versatile multi-criteria decision-making tool introduced by Saaty (1987)
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.
Install ahpsurvey
directly from CRAN:
install.packages("ahpsurvey",repos = "http://cran.us.r-project.org")
Or, install the development version of ahpsurvey
from Github with:
# install.packages("devtools")devtools::install_github("frankiecho/ahpsurvey")
The ahpsurvey
allows one to input a data.frame
consisting of
pairwise comparisons data collected through questionnaires and output an
informative output of the aggregated priorities of all observations, the
individual priorities, consistency ratios, and the most inconsistent
pairwise comparisons.
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
Take a data.frame
like that above and calculate the aggregated
priorities of the 200 decision-makers.
## Define the attributes usedoutput <- ahp(city200, atts <- c("cult", "fam", "house", "jobs", "trans"), negconvert = TRUE, agg = TRUE)#> [1] "Number of observations censored = 0"output$aggpref#> AggPref SD.AggPref#> cult 0.15261018 0.033564038#> fam 0.44827276 0.057695635#> house 0.07052519 0.008844754#> jobs 0.27579123 0.053734270#> trans 0.03965027 0.006700507
And can show the detailed individual priorities of the 200 decision-makers and the consistency ratio of each decision-maker using that list:
head(output$indpref)[1:6]#> cult fam house jobs trans CR#> 1 0.1709466 0.4587181 0.08547330 0.2507636 0.03409845 0.06125366#> 2 0.2291009 0.3935620 0.08292558 0.2531962 0.04121537 0.02962755#> 3 0.1540045 0.4921905 0.08239372 0.2213908 0.05002052 0.06327989#> 4 0.1242495 0.4634863 0.06162027 0.3159930 0.03465092 0.09308731#> 5 0.1521676 0.3556904 0.07239889 0.3748108 0.04493236 0.10604443#> 6 0.1536560 0.4738939 0.07106456 0.2516808 0.04970479 0.10740624
Further arguments allow you to specify the aggregation method, impute missing values and identify and correct inconsistent responses.
An overview of the functions in this package are as follows:
ahp
: A canned AHP routineahp.mat
: Generate AHP pairwise matrices from survey dataahp.indpref
: Priority weights of individual decision-makersahp.aggpref
: Aggregate individual priorities (AIP)ahp.aggjudge
: Aggregate individual judgements (AIJ)ahp.cr
: Saaty’s Consistency Ratioahp.error
: The product between the pairwise comparison value and
pj/piahp.pwerror
: Finds the pairwise comparisons with the maximum
amount of inconsistencyahp.missing
: Impute missing pairwise comparsionsahp.harker
: Replace inconsistent pairwise comparisonsFor 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
.
I have plans to add the following features in the future, perhaps after I finish writing up my masters thesis :-(
Please let me know if there are any features which could be useful to you in a feature request or contribution.
This project is licensed under the MIT License.
Allows for an ID column/ columns for the ahp
routine so that the output can preserve some column with an individual identifier(s).
Adds an col
argument for ahp
to specify the columns and its order of the pairwise comparison variables directly to ahp
.
Added ahp.ri
, which allows users to self-generate random indices used to calculate the consistency ratio.
Replaced default values of RI in ahp.cr
with values generated in ahp.ri
with 500000 simulations.
Added a new canned routine, ahp
, which provides a detailed output using some of the best functions in ahpsurvey
.
Edited wording in the documentation to make it a bit more consistent.
Fixed a bug where ahp.missing
throws an error when a mix of complete and incomplete pairwise matrices is passed through it.
Fixed the html vignette file
Responded to CRAN maintainer Uwe Ligges's comments:
Added reference about the method in the Description field in the form Authors (year) doi:.....
Corrected the MIT license based on the CRAN template
Removed the eigen
option in the ahp.indpref
and ahp.aggpref
functions -- now users have to specify eigen
with method = "eigen"
.
Fresh release
PDF vignette to be added for accurate output of the vignette
Checked with no errors on build_win()
and on local machine running OSX High Sierra