原文地址
https://www.r-bloggers.com/analytic-hierarchy-process-ahp-with-the-ahp-package/
Tom Dick and Harry problem
問題描述
以下內(nèi)容來自維基百科
This example describes the use of the AHP in choosing a leader for a company whose founder is about to retire. There are several competing candidates and several competing criteria for choosing the most suitable one. By using the AHP, the board of directors is able to choose the best candidate in a rational, transparent way that can be examined and understood by all concerned.
簡單理解：公司領(lǐng)導(dǎo)人退休太惠，需要選舉新的領(lǐng)導(dǎo)人淮蜈。為了使選舉結(jié)果公開透明、使人信服哼丈，需要一定的方法窃款。這里選擇的是AHP（analytic hierarchy process層次分析法）踩寇。
- 目標(biāo)是選擇公司領(lǐng)導(dǎo)人监徘；
- 評(píng)價(jià)的標(biāo)準(zhǔn)教育背景Education晋修，Experience經(jīng)驗(yàn)，Charisma領(lǐng)導(dǎo)力凰盔，Age
- 3個(gè)候選人Tom墓卦，Dick，Harry
AHP層次分析法
AHP is a method for multi-criteria decision making that breaks the problem down based on decision criteria, subcriteria, and alternatives that could satisfy a particular goal. The criteria are compared to one another, the alternatives are compared to one another based on how well they comparatively satisfy the subcriteria, and then the subcriteria are examined in terms of how well they satisfy the higher-level criteria. The Tom-Dick-Harry problem is a simple hierarchy: only one level of criteria separates the goal (“Choose the Most Suitable Leader”) from the alternatives (Tom, Dick, or Harry):
上面這段話自己還看不太明白户敬，反正大體的意思就是運(yùn)用這個(gè)方法可以幫你選出最合適的領(lǐng)導(dǎo)人落剪。
R語言里有一個(gè)現(xiàn)成的包可以來做這個(gè)事情ahp
安裝方法
devtools::install_github("gluc/ahp", build_vignettes = TRUE)
安裝過程有時(shí)候會(huì)報(bào)錯(cuò)cannot remove prior installation of package ‘Rcpp’,刪掉舊的包，手動(dòng)安裝一遍就可以了
但是準(zhǔn)備輸入文件太麻煩尿庐，要求的是YAML格式忠怖。下面是ahp包中提供的示例文件
Version: 2.0
#########################
# Alternatives Section
#
Alternatives: &alternatives
# Here, we list all the alternatives, together with their attributes.
# We can use these attributes later in the file when defining
# preferenceFunctions. The attributes can be quantitative or
# qualitative.
Accord Sedan:
price: 20360
mpg: 31
passengers: 5
cargo: 14
curb weight: 3289
safety class: Midsize Car
crash rating: 4 in Side Impact Front
residual value: 0.52
Accord Hybrid:
price: 31090
mpg: 35
passengers: 5
cargo: 14
curb weight: 3501
safety class: Midsize Car
crash rating: 4 in Side Impact Front
residual value: 0.46
Pilot:
price: 27595
mpg: 22
passengers: 8
cargo: 87.6
curb weight: 4264
safety class: Midsize SUV
crash rating: 4 in Rollover
residual value: 0.4
CR-V:
price: 20700
mpg: 27
passengers: 5
cargo: 72.9
curb weight: 3389
safety class: Small SUV
crash rating: 4 in Rollover
residual value: 0.55
Element:
price: 18980
mpg: 25
passengers: 4
cargo: 74.6
curb weight: 3433
safety class: Small SUV
crash rating: 3 in Rollover
residual value: 0.48
Odyssey:
price: 25645
mpg: 26
passengers: 8
cargo: 147.4
curb weight: 4385
safety class: Minivan
crash rating: All 5 Stars
residual value: 0.48
#
# End of Alternatives Section
#####################################
#####################################
# Goal Section
#
Goal:
# The goal spans a tree of criteria and the alternatives
name: Buy Car
description: >
This is a classic single decision maker problem. It models
the situation facing by a family that wants to buy a new car.
author: unknown
preferences:
# preferences are typically defined pairwise
# 1 means: A is equal to B
# 9 means: A is highly preferrable to B
# 1/9 means: B is highly preferrable to A
pairwise:
- [Cost, Safety, 3]
- [Cost, Style, 7]
- [Cost, Capacity, 3]
- [Safety, Style, 9]
- [Safety, Capacity, 1]
- [Style, Capacity, 1/7]
children:
Cost:
preferences:
pairwise:
- [Purchase Price, Fuel Cost, 2]
- [Purchase Price, Maintenance Cost, 5]
- [Purchase Price, Resale Value, 3]
- [Fuel Cost, Maintenance Cost, 2]
- [Fuel Cost, Resale Value, 2]
- [Maintenance Cost, Resale Value, 1/2]
children:
Purchase Price:
preferences:
pairwise:
- [Accord Sedan, Accord Hybrid, 9]
- [Accord Sedan, Pilot, 9]
- [Accord Sedan, CR-V, 1]
- [Accord Sedan, Element, 1/2]
- [Accord Sedan, Odyssey, 5]
- [Accord Hybrid, Pilot, 1]
- [Accord Hybrid, CR-V, 1/9]
- [Accord Hybrid, Element, 1/9]
- [Accord Hybrid, Odyssey, 1/7]
- [Pilot, CR-V, 1/9]
- [Pilot, Element, 1/9]
- [Pilot, Odyssey, 1/7]
- [CR-V, Element, 1/2]
- [CR-V, Odyssey, 5]
- [Element, Odyssey, 6]
children: *alternatives
# We don't need to retype the alternatives here. Instead
# we can simply make a reference to the alternatives anchor
# defined in the alternatives section of the file.
Fuel Cost:
# Alternatively to the pairwise preferences, you
# can define a preference function. This function
# is in R syntax, and needs to have two arguments.
# The Calculate method will pass all combinations
# of alternatives to this function, and the function
# is expected to return the pairwise preference, i.e.
# a number between 1/9 and 9.
preferences:
pairwiseFunction:
function(a1, a2) min(9, max(1/9, a2$mpg/a1$mpg))
children: *alternatives
Maintenance Cost:
preferences:
pairwise:
- [Accord Sedan, Accord Hybrid, 1.5]
- [Accord Sedan, Pilot, 4]
- [Accord Sedan, CR-V, 4]
- [Accord Sedan, Element, 4]
- [Accord Sedan, Odyssey, 5]
- [Accord Hybrid, Pilot, 4]
- [Accord Hybrid, CR-V, 4]
- [Accord Hybrid, Element, 4]
- [Accord Hybrid, Odyssey, 5]
- [Pilot, CR-V, 1]
- [Pilot, Element, 1.2]
- [Pilot, Odyssey, 1]
- [CR-V, Element, 1]
- [CR-V, Odyssey, 3]
- [Element, Odyssey, 2]
children: *alternatives
Resale Value:
preferences:
pairwiseFunction: >
GetResalePreference <- function(a1, a2) {
if (a1$`residual value` < a2$`residual value`) return (1/GetResalePreference(a2, a1))
ratio <- a1$`residual value` / a2$`residual value`
if (ratio < 1.05) return (1)
if (ratio < 1.1) return (2)
if (ratio < 1.15) return (3)
if (ratio < 1.2) return (4)
if (ratio < 1.25) return (5)
return (5)
}
children: *alternatives
Safety:
preferences:
pairwise:
- [Accord Sedan, Accord Hybrid, 1]
- [Accord Sedan, Pilot, 5]
- [Accord Sedan, CR-V, 7]
- [Accord Sedan, Element, 9]
- [Accord Sedan, Odyssey, 1/3]
- [Accord Hybrid, Pilot, 5]
- [Accord Hybrid, CR-V, 7]
- [Accord Hybrid, Element, 9]
- [Accord Hybrid, Odyssey, 1/3]
- [Pilot, CR-V, 2]
- [Pilot, Element, 9]
- [Pilot, Odyssey, 1/8]
- [CR-V, Element, 2]
- [CR-V, Odyssey, 1/8]
- [Element, Odyssey, 1/9]
children: *alternatives
Style:
preferences:
pairwise:
- [Accord Sedan, Accord Hybrid, 1]
- [Accord Sedan, Pilot, 7]
- [Accord Sedan, CR-V, 5]
- [Accord Sedan, Element, 9]
- [Accord Sedan, Odyssey, 6]
- [Accord Hybrid, Pilot, 7]
- [Accord Hybrid, CR-V, 5]
- [Accord Hybrid, Element, 9]
- [Accord Hybrid, Odyssey, 6]
- [Pilot, CR-V, 1/6]
- [Pilot, Element, 3]
- [Pilot, Odyssey, 1/3]
- [CR-V, Element, 7]
- [CR-V, Odyssey, 5]
- [Element, Odyssey, 1/5]
children: *alternatives
Capacity:
preferences:
pairwise:
- [Cargo Capacity, Passenger Capacity, 1/5]
children:
Cargo Capacity:
preferences:
pairwiseFunction: >
CargoPreference <- function(a1, a2) {
if (a1$cargo < a2$cargo) return (1/CargoPreference(a2, a1))
ratio <- a1$cargo / a2$cargo
if (ratio < 3) return (1)
if (ratio < 8) return (2)
return (3)
}
children: *alternatives
Passenger Capacity:
preferences:
pairwise:
- [Accord Sedan, Accord Hybrid, 1]
- [Accord Sedan, Pilot, 1/2]
- [Accord Sedan, CR-V, 1]
- [Accord Sedan, Element, 3]
- [Accord Sedan, Odyssey, 1/2]
- [Accord Hybrid, Pilot, 1/2]
- [Accord Hybrid, CR-V, 1]
- [Accord Hybrid, Element, 3]
- [Accord Hybrid, Odyssey, 1/2]
- [Pilot, CR-V, 2]
- [Pilot, Element, 6]
- [Pilot, Odyssey, 1]
- [CR-V, Element, 3]
- [CR-V, Odyssey, 1/2]
- [Element, Odyssey, 1/6]
children: *alternatives
#
# End of Goal Section
#####################################
準(zhǔn)備這個(gè)輸入文件應(yīng)該是非常麻煩，如何將我們自己的數(shù)據(jù)整理成這種格式可能得費(fèi)一番功夫了屁倔！
#########################
# Alternatives Section
# THIS IS FOR The Tom, Dick, & Harry problem at
#
Alternatives: &alternatives
# 1= not well; 10 = best possible
# Your assessment based on the paragraph descriptions may be different.
Tom:
age: 50
experience: 7
education: 4
leadership: 10
Dick:
age: 60
experience: 10
education: 6
leadership: 6
Harry:
age: 30
experience: 5
education: 8
leadership: 6
#
# End of Alternatives Section
#####################################
# Goal Section
#
Goal:
# A Goal HAS preferences (within-level comparison) and HAS Children (items in level)
name: Choose the Most Suitable Leader
preferences:
# preferences are defined pairwise
# 1 means: A is equal to B
# 9 means: A is highly preferable to B
# 1/9 means: B is highly preferable to A
- [Experience, Education, 4]
- [Experience, Charisma, 3]
- [Experience, Age, 7]
- [Education, Charisma, 1/3]
- [Education, Age, 3]
- [Age, Charisma, 1/5]
children:
Experience:
preferences:
- [Tom, Dick, 1/4]
- [Tom, Harry, 4]
- [Dick, Harry, 9]
children: *alternatives
Education:
preferences:
- [Tom, Dick, 3]
- [Tom, Harry, 1/5]
- [Dick, Harry, 1/7]
children: *alternatives
Charisma:
preferences:
- [Tom, Dick, 5]
- [Tom, Harry, 9]
- [Dick, Harry, 4]
children: *alternatives
Age:
preferences:
- [Tom, Dick, 1/3]
- [Tom, Harry, 5]
- [Dick, Harry, 9]
children: *alternatives
#
# End of Goal Section
#####################################
接下來用這個(gè)文件來做AHP分析
myAhp<-Load("../Desktop/AHP_practice.txt.txt")
遇到了報(bào)錯(cuò)
Error in value[[3L]](cond) :
Could not load ahp model. Exception caught when converting into a data.tree: Error in doTryCatch(return(expr), name, parentenv, handler): Could not load ahp model. Could not find Version
In addition: Warning message:
In readChar(ahpFile, file.info(ahpFile)$size) :
can only read in bytes in a non-UTF-8 MBCS locale
暫時(shí)還不知道如何解決
重復(fù)ahp報(bào)自帶的例子
devtools::install_github("gluc/ahp")
library(ahp)
myAhp<-Load("../Desktop/AHP_practice.txt.txt")
??LoadFile
ahp::Load()
ahpFile<-system.file("extdata","car.ahp",package="ahp")
carAhp<-Load(ahpFile)
Calculate(carAhp)
Visualize(carAhp)
Analyze(carAhp)
AnalyzeTable(carAhp)
輸出結(jié)果
image.png
image.png
輸出結(jié)果竟然是圖片形式展示表格Ｄ杂帧Ｄ弘省锐借！
結(jié)果里好像也沒有給出最終的決策結(jié)果呀！可能是有其他函數(shù)實(shí)現(xiàn)的嗎往衷？
結(jié)果里給出了各個(gè)變量的權(quán)重
本篇文章的目的：知道有這么個(gè)方法钞翔，知道有這個(gè)R包可以實(shí)現(xiàn)！研究研究如何將自己的數(shù)據(jù)轉(zhuǎn)化為輸入文件的格式席舍！
歡迎大家關(guān)注我的公眾號(hào) 小明的數(shù)據(jù)分析筆記本
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