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:
Or, install the development version of ahpsurvey from Github with:
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 -6Take a data.frame like that above and calculate the aggregated priorities of the 200 decision-makers.
## Define the attributes used
output <- ahp(city200, atts <- c("cult", "fam", "house", "jobs", "trans"), negconvert = TRUE, agg = TRUE)
#> [1] "Number of observations censored = 0"
output$aggpref
#> AggPref SD.AggPref
#> cult 0.15261018 0.033564038
#> fam 0.44827276 0.057695635
#> house 0.07052519 0.008844754
#> jobs 0.27579123 0.053734270
#> trans 0.03965027 0.006700507And can show the detailed individual priorities of the 200 decision-makers and the consistency ratio of each decision-maker using that list:
head(output$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 0.10740624Further 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.