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项目作者： frankiecho

项目描述 ：
Analytic Hierarchy Process for Survey Data in R
高级语言： R
项目地址: git://github.com/frankiecho/ahpsurvey.git
创建时间： 2018-09-08T04:33:30Z
项目社区：https://github.com/frankiecho/ahpsurvey
开源协议：Other
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ahpsurvey

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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:

  1. install.packages("ahpsurvey",repos = "http://cran.us.r-project.org")

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

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

Usage

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.

  1. library(ahpsurvey)
  2. library(magrittr)
  4. data(city200)
  5. city200 %>% head()
  6. #>   cult_fam cult_house cult_jobs cult_trans fam_house fam_jobs fam_trans
  7. #> 1        2         -2         2         -6        -4       -4        -8
  8. #> 2        2         -4         1         -4        -4       -2        -8
  9. #> 3        4         -2         1         -3        -7       -3        -5
  10. #> 4        8         -4         3         -4        -8        1        -7
  11. #> 5        3         -3         5         -6        -8        1        -4
  12. #> 6        6         -4         2         -4        -7       -2        -4
  13. #>   house_jobs house_trans jobs_trans
  14. #> 1          4          -3         -8
  15. #> 2          4          -3         -7
  16. #> 3          4          -3         -6
  17. #> 4          4          -3         -9
  18. #> 5          4          -3         -6
  19. #> 6          4          -3         -6

Take a data.frame like that above and calculate the aggregated
priorities of the 200 decision-makers.

  1. ## Define the attribute used
  2. output <- ahp(city200, atts <- c("cult", "fam", "house", "jobs", "trans"), negconvert = TRUE, agg = TRUE)
  3. #> [1] "Number of observations censored = 0"
  4. output$aggpref
  5. #>          AggPref  SD.AggPref
  6. #> cult  0.15261018 0.033564038
  7. #> fam   0.44827276 0.057695635
  8. #> house 0.07052519 0.008844754
  9. #> jobs  0.27579123 0.053734270
  10. #> 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:

  1. head(output$indpref)[1:6]
  2. #>        cult       fam      house      jobs      trans         CR
  3. #> 1 0.1709466 0.4587181 0.08547330 0.2507636 0.03409845 0.06125366
  4. #> 2 0.2291009 0.3935620 0.08292558 0.2531962 0.04121537 0.02962755
  5. #> 3 0.1540045 0.4921905 0.08239372 0.2213908 0.05002052 0.06327989
  6. #> 4 0.1242495 0.4634863 0.06162027 0.3159930 0.03465092 0.09308731
  7. #> 5 0.1521676 0.3556904 0.07239889 0.3748108 0.04493236 0.10604443
  8. #> 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.

Functions

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

  • ahp: A canned AHP routine
  • 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 comparisons
  • ahp.md: Replace inconsistent pairwise comparisons with the maximum deviation method

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.

Author

License

This project is licensed under the MIT License.