Analytic Hierarchy Process for Survey Data in R
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 attribute used
output <- 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 andahp.pwerror
: Finds the pairwise comparisons with the maximumahp.missing
: Impute missing pairwise comparisonsahp.md
: Replace inconsistent pairwise comparisons with the maximum deviation methodFor 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 usingggplot2
.
This project is licensed under the MIT License.