今日代码(200714)--主客观求指标权重及求城市得分

代码记录

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主客观求指标权重及求城市得分

前言

有22个指标和71个城市的10年数据，现在利用主成分分析法求出客观权重，并结合主管权重得出总权重，最后利用权重与标准化数值求出10年的得分。

前期准备

设置工作路径,读取数据

workL = "C:/Users/goatbishop/Desktop/data"
setwd(workL)
getwd()

library(magrittr)

data <- read.csv("data0710.csv", stringsAsFactors = F)
mendW <- read.table("weight2.txt") %>% c() %>% unlist()
mendW <- mendW/sum(mendW)

方法区

myStand是计算权重用的正向指标标准化化, downStand是计算权重用的负向指标的标准化,upStand2是计算用的正指标标准化方法,upStand2是计算用的负指标标准化方法, weightDemo是提取权重,相当于提取前10个主成分，再进行计算，这里没有依据特征值大小或者累计贡献率提取主成分数，仅仅是较为随意的选取前10个主成分，请注意排查错误！

####方法区####
myStand <- function(x) {
  newx = (x-mean(x))/sd(x)
  return(newx)
}

downStand  <- function(x) {
  newx = myStand(-x)
  return(newx)
}

upStand2 <- function(x) {
  newx = (x-min(x) + 0.01)/(max(x)-min(x))
  return(newx)
}

downStand2  <- function(x) {
  newx = (max(x)-x + 0.01)/(max(x)-min(x))
  return(newx)
}

weightDemo <- function(x) {
  #这个问题以后再解决
  R <- (t(x) %*% x)/ dim(x)[1]
  e <- eigen(R, symmetric=T)
  pcMa <- abs(e$vectors)
  sqrtValue <- e$values^(0.5)
  pcVa <- e$values[1:10]/sum(e$values[1:10])
  #print(pcVa)
  weightTemp <- t(t(pcMa) / sqrtValue)[, 1:10]
  weight <- weightTemp %*% pcVa
  weight <- weight/sum(weight)
  return(weight)
}

求权重以及得分

构造权重和得分矩阵,并求得权重和得分

#构造权重矩阵
weight <- matrix(0 , ncol = 10, nrow  = 22)
colnames(weight) <- c(2018:2009)

weightObj <- matrix(0 , ncol = 10, nrow  = 22)
colnames(weightObj) <- c(2018:2009)

#构造得分矩阵
score <- matrix(0 , ncol = 10, nrow  = 71)
colnames(score) <- c(2018:2009)

count = 0
for (y in c(2018:2009)) {
  count = count + 1
  tempdata <- subset(data, year == y)
  tempdata = tempdata[, -c(1:3)]
  tempData_up <- apply(tempdata[, -c(7, 10, 12, 17, 18)], 2, myStand)
  #正向
  tempData_down <- apply(tempdata[, c(7, 10, 12, 17, 18)], 2, downStand)
  #负向
  newData <- cbind(tempData_up, tempData_down)
  
  tempData_up2 <- apply(tempdata[, -c(7, 10, 12, 17, 18)], 2, upStand2 )
  tempData_down2 <- apply(tempdata[, c(7, 10, 12, 17, 18)], 2, downStand2)
  newData2 <- cbind(tempData_up2, tempData_down2)
  ## 计算权重
  w <- weightDemo(newData)
  #print(w)
  wave <- (w + mendW)/2
  #print(wave)
  
  #列中的所有行加总
  weightObj[, count] <- w
  weight[, count] <- wave
  #print(wave)
  
  ##计算得分
  s <- newData2 %*% wave
  score[, count] <- s
}

rownames(weightObj) <- colnames(newData)
rownames(weight) <- colnames(newData)
rownames(score) <- data[c(1:71), "city"]

写出数据

write.csv(weight, "weightP.csv")
write.csv(weightObj, "weightObj.csv")
write.csv(score, "scoreP.csv")