基于熵权法的未确知测度评价模型
理论自己找个论文看看就可以了，直接放代码与数据，数据来源于统计年鉴
这里使用三个表具体如下：
原始数据表
分级标准表
分级评价指标表
其中，分级评价指标表是原始数据表经过归一化处理操作后所得，分级标准表是为了方便描述所建立的特殊分级标准。
正题开始，主要有四步
第一步，熵权法计算权重，实际运用可以使用其它权重计算方法
library(readxl)
library(dplyr)
#重新设置R工作路径
setwd('D:/重要文件/22数学建模校赛/过程/问题三')
#读取指标数据
data <- read_xlsx('数据.xlsx',col_names = T)
#去除无用列
data$年份 <- NULL
#熵权法计算指标权重
#定义正向指标归一化处理函数
svf1 <- function(x){(x-min(x))/(max(x)-min(x))}
#定义逆向指标归一化处理函数
svf2 <- function(x){(max(x)-x)/(max(x)-min(x))}
#归一化处理
sv1 <- apply(data[,c(1,2,3,10,11,12)],2,svf1)
sv2 <- apply(data[,-c(1,2,3,10,11,12)],2,svf2)
a <- sv1[,c(1:3)]
b <- sv2[,c(1:6)]
c <- sv1[,c(4:6)]
d <- sv2[,c(7:13)]
svt <- cbind(a,b,c,d)
e <- t(svt)
colnames(e) <- c('2010','2011','2012','2013','2014','2015','2016','2017','2018','2019','2020')
write.csv(e,file='分级评价指标.csv')
#计算第j个指标下第i个样本值占该指标的比重
sgf <- function(x){y <- c(sv1)
for(i in 1:length(x))y[i] = x[i]/sum(x[])
return(y)}
sg <- apply(svt,2,sgf)
#计算信息熵
lef <- function(x)
{y <- c(x)for(i in 1:length(x)){if(y[i] == 0){y[i] = 0}else{y[i] = x[i] * log(x[i])}}return(y)
}
le <- apply(svt,2,lef)
k <- 1/log(length(le[,1]))
#计算第j项指标的熵值
e <- -k * colSums(le)
#计算信息熵差异
d <- 1-e
#计算各项指标权重
w <- d/sum(d)
write.csv(w,file='各项指标权重.csv')第二步，计算单指标测度函数矩阵
#计算单指标测度函数矩阵
setwd('D:/重要文件/22数学建模校赛/过程/问题三')
#读入数据
data1 <- read_excel('分级标准.xlsx')
data2 <- read_excel('分级评价指标.xlsx')
#数据处理
data1[,c(1:3)] <- NULL
data2[,c(1,2)] <- NULL
x1 <- data2[,1]
x2 <- data2[,2]
x3 <- data2[,3]
x4 <- data2[,4]
x5 <- data2[,5]
x6 <- data2[,6]
x7 <- data2[,7]
x8 <- data2[,8]
x9 <- data2[,9]
x10 <- data2[,10]
x11 <- data2[,11]
#转置
x1 <- t(x1)
x2 <- t(x2)
x3 <- t(x3)
x4 <- t(x4)
x5 <- t(x5)
x6 <- t(x6)
x7 <- t(x7)
x8 <- t(x8)
x9 <- t(x9)
x10 <- t(x10)
x11 <- t(x11)
#定义单指标测度函数矩阵
#if条件随评分标准变动，多个指标不同标准需要建立多个函数判断
fv <- function(x)
{f <- matrix(rep(0,95),19,5)for(i in 1:length(x)){if(x[,i]==0){f[i,] <- c(0,0,0,0,1)}else if(x[,i]==0.2){f[i,] <- c(0,0,0,0,1)}else if(x[,i]==0.4){f[i,] <- c(0,0,0,1,0)}else if(x[,i]==0.6){f[i,] <- c(0,0,1,0,0)}else if(x[,i]==0.8){f[i,] <- c(0,1,0,0,0)}else if(x[,i]==1){f[i,] <- c(1,0,0,0,0)}else if(0第三步，计算多指标综合测度矩阵，其中计算各个年份矩阵的各个元素值的代码，可自行编写循坏进行代替，这里有点冗余了
#计算多指标测度矩阵与各个样本得分
w <- read.csv('各项指标权重.csv')
setwd('D:/重要文件/22数学建模校赛/过程/问题三/测度矩阵')
#读入数据与处理
m1 <- read.csv('2010.csv')
m2 <- read.csv('2011.csv')
m3 <- read.csv('2012.csv')
m4 <- read.csv('2013.csv')
m5 <- read.csv('2014.csv')
m6 <- read.csv('2015.csv')
m7 <- read.csv('2016.csv')
m8 <- read.csv('2017.csv')
m9 <- read.csv('2018.csv')
m10 <- read.csv('2019.csv')
m11 <- read.csv('2020.csv')
m1[,1] <- NULL
m2[,1] <- NULL
m3[,1] <- NULL
m4[,1] <- NULL
m5[,1] <- NULL
m6[,1] <- NULL
m7[,1] <- NULL
m8[,1] <- NULL
m9[,1] <- NULL
m10[,1] <- NULL
m11[,1] <- NULL
w[,1] <- NULL
#生成空矩阵
n <- matrix(rep(0,55),11,5)
#计算2010年各元素值
n[1,1] <- w[1,]* m1[1,1] + w[2,]* m1[2,1]+w[3,]* m1[3,1]+w[4,]* m1[4,1]+w[5,]* m1[5,1]+w[6,]* m1[6,1]+w[7,]* m1[7,1]+w[8,]* m1[8,1]+w[9,]* m1[9,1]+w[10,]* m1[10,1]+w[11,]* m1[11,1]+w[12,]* m1[12,1]+w[13,]* m1[13,1]+w[14,]* m1[14,1]+w[15,]* m1[15,1]+w[16,]* m1[16,1]+w[17,]* m1[17,1]+w[18,]* m1[18,1]+w[19,]* m1[19,1]n[1,2] <- w[1,]* m1[1,2] + w[2,]* m1[2,2]+w[3,]* m1[3,2]+w[4,]* m1[4,2]+w[5,]* m1[5,2]+w[6,]* m1[6,2]+w[7,]* m1[7,2]+w[8,]* m1[8,2]+w[9,]* m1[9,2]+w[10,]* m1[10,2]+w[11,]* m1[11,2]+w[12,]* m1[12,2]+w[13,]* m1[13,2]+w[14,]* m1[14,2]+w[15,]* m1[15,2]+w[16,]* m1[16,2]+w[17,]* m1[17,2]+w[18,]* m1[18,2]+w[19,]* m1[19,2]n[1,3] <- w[1,]* m1[1,3] + w[2,]* m1[2,3]+w[3,]* m1[3,3]+w[4,]* m1[4,3]+w[5,]* m1[5,3]+w[6,]* m1[6,3]+w[7,]* m1[7,3]+w[8,]* m1[8,3]+w[9,]* m1[9,3]+w[10,]* m1[10,3]+w[11,]* m1[11,3]+w[12,]* m1[12,3]+w[13,]* m1[13,3]+w[14,]* m1[14,3]+w[15,]* m1[15,3]+w[16,]* m1[16,3]+w[17,]* m1[17,3]+w[18,]* m1[18,3]+w[19,]* m1[19,3]n[1,4] <- w[1,]* m1[1,4] + w[2,]* m1[2,4]+w[3,]* m1[3,4]+w[4,]* m1[4,4]+w[5,]* m1[5,4]+w[6,]* m1[6,4]+w[7,]* m1[7,4]+w[8,]* m1[8,4]+w[9,]* m1[9,4]+w[10,]* m1[10,4]+w[11,]* m1[11,4]+w[12,]* m1[12,4]+w[13,]* m1[13,1]+w[14,]* m1[14,4]+w[15,]* m1[15,4]+w[16,]* m1[16,4]+w[17,]* m1[17,4]+w[18,]* m1[18,4]+w[19,]* m1[19,4]n[1,5] <- w[1,]* m1[1,5] + w[2,]* m1[2,5]+w[3,]* m1[3,5]+w[4,]* m1[4,5]+w[5,]* m1[5,5]+w[6,]* m1[6,5]+w[7,]* m1[7,5]+w[8,]* m1[8,5]+w[9,]* m1[9,5]+w[10,]* m1[10,5]+w[11,]* m1[11,5]+w[12,]* m1[12,5]+w[13,]* m1[13,1]+w[14,]* m1[14,5]+w[15,]* m1[15,5]+w[16,]* m1[16,5]+w[17,]* m1[17,5]+w[18,]* m1[18,5]+w[19,]* m1[19,5]
#计算2011年各元素值
n[2,1] <- w[1,]* m2[1,1] + w[2,]* m2[2,1]+w[3,]* m2[3,1]+w[4,]* m2[4,1]+w[5,]* m2[5,1]+w[6,]* m2[6,1]+w[7,]* m2[7,1]+w[8,]* m2[8,1]+w[9,]* m2[9,1]+w[10,]* m2[10,1]+w[11,]* m2[11,1]+w[12,]* m2[12,1]+w[13,]* m2[13,1]+w[14,]* m2[14,1]+w[15,]* m2[15,1]+w[16,]* m2[16,1]+w[17,]* m2[17,1]+w[18,]* m2[18,1]+w[19,]* m2[19,1]n[2,2] <- w[1,]* m2[1,2] + w[2,]* m2[2,2]+w[3,]* m2[3,2]+w[4,]* m2[4,2]+w[5,]* m2[5,2]+w[6,]* m2[6,2]+w[7,]* m2[7,2]+w[8,]* m2[8,2]+w[9,]* m2[9,2]+w[10,]* m2[10,2]+w[11,]* m2[11,2]+w[12,]* m2[12,2]+w[13,]* m2[13,2]+w[14,]* m2[14,2]+w[15,]* m2[15,2]+w[16,]* m2[16,2]+w[17,]* m2[17,2]+w[18,]* m2[18,2]+w[19,]* m2[19,2]n[2,3] <- w[1,]* m2[1,3] + w[2,]* m2[2,3]+w[3,]* m2[3,3]+w[4,]* m2[4,3]+w[5,]* m2[5,3]+w[6,]* m2[6,3]+w[7,]* m2[7,3]+w[8,]* m2[8,3]+w[9,]* m2[9,3]+w[10,]* m2[10,3]+w[11,]* m2[11,3]+w[12,]* m2[12,3]+w[13,]* m2[13,3]+w[14,]* m2[14,3]+w[15,]* m2[15,3]+w[16,]* m2[16,3]+w[17,]* m2[17,3]+w[18,]* m2[18,3]+w[19,]* m2[19,3]n[2,4] <- w[1,]* m2[1,4] + w[2,]* m2[2,4]+w[3,]* m2[3,4]+w[4,]* m2[4,4]+w[5,]* m2[5,4]+w[6,]* m2[6,4]+w[7,]* m2[7,4]+w[8,]* m2[8,4]+w[9,]* m2[9,4]+w[10,]* m2[10,4]+w[11,]* m2[11,4]+w[12,]* m2[12,4]+w[13,]* m2[13,1]+w[14,]* m2[14,4]+w[15,]* m2[15,4]+w[16,]* m2[16,4]+w[17,]* m2[17,4]+w[18,]* m2[18,4]+w[19,]* m2[19,4]n[2,5] <- w[1,]* m2[1,5] + w[2,]* m2[2,5]+w[3,]* m2[3,5]+w[4,]* m2[4,5]+w[5,]* m2[5,5]+w[6,]* m2[6,5]+w[7,]* m2[7,5]+w[8,]* m2[8,5]+w[9,]* m2[9,5]+w[10,]* m2[10,5]+w[11,]* m2[11,5]+w[12,]* m2[12,5]+w[13,]* m2[13,1]+w[14,]* m2[14,5]+w[15,]* m2[15,5]+w[16,]* m2[16,5]+w[17,]* m2[17,5]+w[18,]* m2[18,5]+w[19,]* m2[19,5]
#计算2012年各元素值
n[3,1] <- w[1,]* m3[1,1] + w[2,]* m3[2,1]+w[3,]* m3[3,1]+w[4,]* m3[4,1]+w[5,]* m3[5,1]+w[6,]* m3[6,1]+w[7,]* m3[7,1]+w[8,]* m3[8,1]+w[9,]* m3[9,1]+w[10,]* m3[10,1]+w[11,]* m3[11,1]+w[12,]* m3[12,1]+w[13,]* m3[13,1]+w[14,]* m3[14,1]+w[15,]* m3[15,1]+w[16,]* m3[16,1]+w[17,]* m3[17,1]+w[18,]* m3[18,1]+w[19,]* m3[19,1]n[3,2] <- w[1,]* m3[1,2] + w[2,]* m3[2,2]+w[3,]* m3[3,2]+w[4,]* m3[4,2]+w[5,]* m3[5,2]+w[6,]* m3[6,2]+w[7,]* m3[7,2]+w[8,]* m3[8,2]+w[9,]* m3[9,2]+w[10,]* m3[10,2]+w[11,]* m3[11,2]+w[12,]* m3[12,2]+w[13,]* m3[13,2]+w[14,]* m3[14,2]+w[15,]* m3[15,2]+w[16,]* m3[16,2]+w[17,]* m3[17,2]+w[18,]* m3[18,2]+w[19,]* m3[19,2]n[3,3] <- w[1,]* m3[1,3] + w[2,]* m3[2,3]+w[3,]* m3[3,3]+w[4,]* m3[4,3]+w[5,]* m3[5,3]+w[6,]* m3[6,3]+w[7,]* m3[7,3]+w[8,]* m3[8,3]+w[9,]* m3[9,3]+w[10,]* m3[10,3]+w[11,]* m3[11,3]+w[12,]* m3[12,3]+w[13,]* m3[13,3]+w[14,]* m3[14,3]+w[15,]* m3[15,3]+w[16,]* m3[16,3]+w[17,]* m3[17,3]+w[18,]* m3[18,3]+w[19,]* m3[19,3]n[3,4] <- w[1,]* m3[1,4] + w[2,]* m3[2,4]+w[3,]* m3[3,4]+w[4,]* m3[4,4]+w[5,]* m3[5,4]+w[6,]* m3[6,4]+w[7,]* m3[7,4]+w[8,]* m3[8,4]+w[9,]* m3[9,4]+w[10,]* m3[10,4]+w[11,]* m3[11,4]+w[12,]* m3[12,4]+w[13,]* m3[13,1]+w[14,]* m3[14,4]+w[15,]* m3[15,4]+w[16,]* m3[16,4]+w[17,]* m3[17,4]+w[18,]* m3[18,4]+w[19,]* m3[19,4]n[3,5] <- w[1,]* m3[1,5] + w[2,]* m3[2,5]+w[3,]* m3[3,5]+w[4,]* m3[4,5]+w[5,]* m3[5,5]+w[6,]* m3[6,5]+w[7,]* m3[7,5]+w[8,]* m3[8,5]+w[9,]* m3[9,5]+w[10,]* m3[10,5]+w[11,]* m3[11,5]+w[12,]* m3[12,5]+w[13,]* m3[13,1]+w[14,]* m3[14,5]+w[15,]* m3[15,5]+w[16,]* m3[16,5]+w[17,]* m3[17,5]+w[18,]* m3[18,5]+w[19,]* m3[19,5]
#计算2013年各元素值
n[4,1] <- w[1,]* m4[1,1] + w[2,]* m4[2,1]+w[3,]* m4[3,1]+w[4,]* m4[4,1]+w[5,]* m4[5,1]+w[6,]* m4[6,1]+w[7,]* m4[7,1]+w[8,]* m4[8,1]+w[9,]* m4[9,1]+w[10,]* m4[10,1]+w[11,]* m4[11,1]+w[12,]* m4[12,1]+w[13,]* m4[13,1]+w[14,]* m4[14,1]+w[15,]* m4[15,1]+w[16,]* m4[16,1]+w[17,]* m4[17,1]+w[18,]* m4[18,1]+w[19,]* m4[19,1]n[4,2] <- w[1,]* m4[1,2] + w[2,]* m4[2,2]+w[3,]* m4[3,2]+w[4,]* m4[4,2]+w[5,]* m4[5,2]+w[6,]* m4[6,2]+w[7,]* m4[7,2]+w[8,]* m4[8,2]+w[9,]* m4[9,2]+w[10,]* m4[10,2]+w[11,]* m4[11,2]+w[12,]* m4[12,2]+w[13,]* m4[13,2]+w[14,]* m4[14,2]+w[15,]* m4[15,2]+w[16,]* m4[16,2]+w[17,]* m4[17,2]+w[18,]* m4[18,2]+w[19,]* m4[19,2]n[4,3] <- w[1,]* m4[1,3] + w[2,]* m4[2,3]+w[3,]* m4[3,3]+w[4,]* m4[4,3]+w[5,]* m4[5,3]+w[6,]* m4[6,3]+w[7,]* m4[7,3]+w[8,]* m4[8,3]+w[9,]* m4[9,3]+w[10,]* m4[10,3]+w[11,]* m4[11,3]+w[12,]* m4[12,3]+w[13,]* m4[13,3]+w[14,]* m4[14,3]+w[15,]* m4[15,3]+w[16,]* m4[16,3]+w[17,]* m4[17,3]+w[18,]* m4[18,3]+w[19,]* m4[19,3]n[4,4] <- w[1,]* m4[1,4] + w[2,]* m4[2,4]+w[3,]* m4[3,4]+w[4,]* m4[4,4]+w[5,]* m4[5,4]+w[6,]* m4[6,4]+w[7,]* m4[7,4]+w[8,]* m4[8,4]+w[9,]* m4[9,4]+w[10,]* m4[10,4]+w[11,]* m4[11,4]+w[12,]* m4[12,4]+w[13,]* m4[13,1]+w[14,]* m4[14,4]+w[15,]* m4[15,4]+w[16,]* m4[16,4]+w[17,]* m4[17,4]+w[18,]* m4[18,4]+w[19,]* m4[19,4]n[4,5] <- w[1,]* m4[1,5] + w[2,]* m4[2,5]+w[3,]* m4[3,5]+w[4,]* m4[4,5]+w[5,]* m4[5,5]+w[6,]* m4[6,5]+w[7,]* m4[7,5]+w[8,]* m4[8,5]+w[9,]* m4[9,5]+w[10,]* m4[10,5]+w[11,]* m4[11,5]+w[12,]* m4[12,5]+w[13,]* m4[13,1]+w[14,]* m4[14,5]+w[15,]* m4[15,5]+w[16,]* m4[16,5]+w[17,]* m4[17,5]+w[18,]* m4[18,5]+w[19,]* m4[19,5]
#计算2014年各元素值
n[5,1] <- w[1,]* m5[1,1] + w[2,]* m5[2,1]+w[3,]* m5[3,1]+w[4,]* m5[4,1]+w[5,]* m5[5,1]+w[6,]* m5[6,1]+w[7,]* m5[7,1]+w[8,]* m5[8,1]+w[9,]* m5[9,1]+w[10,]* m5[10,1]+w[11,]* m5[11,1]+w[12,]* m5[12,1]+w[13,]* m5[13,1]+w[14,]* m5[14,1]+w[15,]* m5[15,1]+w[16,]* m5[16,1]+w[17,]* m5[17,1]+w[18,]* m5[18,1]+w[19,]* m5[19,1]n[5,2] <- w[1,]* m5[1,2] + w[2,]* m5[2,2]+w[3,]* m5[3,2]+w[4,]* m5[4,2]+w[5,]* m5[5,2]+w[6,]* m5[6,2]+w[7,]* m5[7,2]+w[8,]* m5[8,2]+w[9,]* m5[9,2]+w[10,]* m5[10,2]+w[11,]* m5[11,2]+w[12,]* m5[12,2]+w[13,]* m5[13,2]+w[14,]* m5[14,2]+w[15,]* m5[15,2]+w[16,]* m5[16,2]+w[17,]* m5[17,2]+w[18,]* m5[18,2]+w[19,]* m5[19,2]n[5,3] <- w[1,]* m5[1,3] + w[2,]* m5[2,3]+w[3,]* m5[3,3]+w[4,]* m5[4,3]+w[5,]* m5[5,3]+w[6,]* m5[6,3]+w[7,]* m5[7,3]+w[8,]* m5[8,3]+w[9,]* m5[9,3]+w[10,]* m5[10,3]+w[11,]* m5[11,3]+w[12,]* m5[12,3]+w[13,]* m5[13,3]+w[14,]* m5[14,3]+w[15,]* m5[15,3]+w[16,]* m5[16,3]+w[17,]* m5[17,3]+w[18,]* m5[18,3]+w[19,]* m5[19,3]n[5,4] <- w[1,]* m5[1,4] + w[2,]* m5[2,4]+w[3,]* m5[3,4]+w[4,]* m5[4,4]+w[5,]* m5[5,4]+w[6,]* m5[6,4]+w[7,]* m5[7,4]+w[8,]* m5[8,4]+w[9,]* m5[9,4]+w[10,]* m5[10,4]+w[11,]* m5[11,4]+w[12,]* m5[12,4]+w[13,]* m5[13,1]+w[14,]* m5[14,4]+w[15,]* m5[15,4]+w[16,]* m5[16,4]+w[17,]* m5[17,4]+w[18,]* m5[18,4]+w[19,]* m5[19,4]n[5,5] <- w[1,]* m5[1,5] + w[2,]* m5[2,5]+w[3,]* m5[3,5]+w[4,]* m5[4,5]+w[5,]* m5[5,5]+w[6,]* m5[6,5]+w[7,]* m5[7,5]+w[8,]* m5[8,5]+w[9,]* m5[9,5]+w[10,]* m5[10,5]+w[11,]* m5[11,5]+w[12,]* m5[12,5]+w[13,]* m5[13,1]+w[14,]* m5[14,5]+w[15,]* m5[15,5]+w[16,]* m5[16,5]+w[17,]* m5[17,5]+w[18,]* m5[18,5]+w[19,]* m5[19,5]
#计算2015年各元素值
n[6,1] <- w[1,]* m6[1,1] + w[2,]* m6[2,1]+w[3,]* m6[3,1]+w[4,]* m6[4,1]+w[5,]* m6[5,1]+w[6,]* m6[6,1]+w[7,]* m6[7,1]+w[8,]* m6[8,1]+w[9,]* m6[9,1]+w[10,]* m6[10,1]+w[11,]* m6[11,1]+w[12,]* m6[12,1]+w[13,]* m6[13,1]+w[14,]* m6[14,1]+w[15,]* m6[15,1]+w[16,]* m6[16,1]+w[17,]* m6[17,1]+w[18,]* m6[18,1]+w[19,]* m6[19,1]n[6,2] <- w[1,]* m6[1,2] + w[2,]* m6[2,2]+w[3,]* m6[3,2]+w[4,]* m6[4,2]+w[5,]* m6[5,2]+w[6,]* m6[6,2]+w[7,]* m6[7,2]+w[8,]* m6[8,2]+w[9,]* m6[9,2]+w[10,]* m6[10,2]+w[11,]* m6[11,2]+w[12,]* m6[12,2]+w[13,]* m6[13,2]+w[14,]* m6[14,2]+w[15,]* m6[15,2]+w[16,]* m6[16,2]+w[17,]* m6[17,2]+w[18,]* m6[18,2]+w[19,]* m6[19,2]n[6,3] <- w[1,]* m6[1,3] + w[2,]* m6[2,3]+w[3,]* m6[3,3]+w[4,]* m6[4,3]+w[5,]* m6[5,3]+w[6,]* m6[6,3]+w[7,]* m6[7,3]+w[8,]* m6[8,3]+w[9,]* m6[9,3]+w[10,]* m6[10,3]+w[11,]* m6[11,3]+w[12,]* m6[12,3]+w[13,]* m6[13,3]+w[14,]* m6[14,3]+w[15,]* m6[15,3]+w[16,]* m6[16,3]+w[17,]* m6[17,3]+w[18,]* m6[18,3]+w[19,]* m6[19,3]n[6,4] <- w[1,]* m6[1,4] + w[2,]* m6[2,4]+w[3,]* m6[3,4]+w[4,]* m6[4,4]+w[5,]* m6[5,4]+w[6,]* m6[6,4]+w[7,]* m6[7,4]+w[8,]* m6[8,4]+w[9,]* m6[9,4]+w[10,]* m6[10,4]+w[11,]* m6[11,4]+w[12,]* m6[12,4]+w[13,]* m6[13,1]+w[14,]* m6[14,4]+w[15,]* m6[15,4]+w[16,]* m6[16,4]+w[17,]* m6[17,4]+w[18,]* m6[18,4]+w[19,]* m6[19,4]n[6,5] <- w[1,]* m6[1,5] + w[2,]* m6[2,5]+w[3,]* m6[3,5]+w[4,]* m6[4,5]+w[5,]* m6[5,5]+w[6,]* m6[6,5]+w[7,]* m6[7,5]+w[8,]* m6[8,5]+w[9,]* m6[9,5]+w[10,]* m6[10,5]+w[11,]* m6[11,5]+w[12,]* m6[12,5]+w[13,]* m6[13,1]+w[14,]* m6[14,5]+w[15,]* m6[15,5]+w[16,]* m6[16,5]+w[17,]* m6[17,5]+w[18,]* m6[18,5]+w[19,]* m6[19,5]
#计算2016年各元素值
n[7,1] <- w[1,]* m7[1,1] + w[2,]* m7[2,1]+w[3,]* m7[3,1]+w[4,]* m7[4,1]+w[5,]* m7[5,1]+w[6,]* m7[6,1]+w[7,]* m7[7,1]+w[8,]* m7[8,1]+w[9,]* m7[9,1]+w[10,]* m7[10,1]+w[11,]* m7[11,1]+w[12,]* m7[12,1]+w[13,]* m7[13,1]+w[14,]* m7[14,1]+w[15,]* m7[15,1]+w[16,]* m7[16,1]+w[17,]* m7[17,1]+w[18,]* m7[18,1]+w[19,]* m7[19,1]n[7,2] <- w[1,]* m7[1,2] + w[2,]* m7[2,2]+w[3,]* m7[3,2]+w[4,]* m7[4,2]+w[5,]* m7[5,2]+w[6,]* m7[6,2]+w[7,]* m7[7,2]+w[8,]* m7[8,2]+w[9,]* m7[9,2]+w[10,]* m7[10,2]+w[11,]* m7[11,2]+w[12,]* m7[12,2]+w[13,]* m7[13,2]+w[14,]* m7[14,2]+w[15,]* m7[15,2]+w[16,]* m7[16,2]+w[17,]* m7[17,2]+w[18,]* m7[18,2]+w[19,]* m7[19,2]n[7,3] <- w[1,]* m7[1,3] + w[2,]* m7[2,3]+w[3,]* m7[3,3]+w[4,]* m7[4,3]+w[5,]* m7[5,3]+w[6,]* m7[6,3]+w[7,]* m7[7,3]+w[8,]* m7[8,3]+w[9,]* m7[9,3]+w[10,]* m7[10,3]+w[11,]* m7[11,3]+w[12,]* m7[12,3]+w[13,]* m7[13,3]+w[14,]* m7[14,3]+w[15,]* m7[15,3]+w[16,]* m7[16,3]+w[17,]* m7[17,3]+w[18,]* m7[18,3]+w[19,]* m7[19,3]n[7,4] <- w[1,]* m7[1,4] + w[2,]* m7[2,4]+w[3,]* m7[3,4]+w[4,]* m7[4,4]+w[5,]* m7[5,4]+w[6,]* m7[6,4]+w[7,]* m7[7,4]+w[8,]* m7[8,4]+w[9,]* m7[9,4]+w[10,]* m7[10,4]+w[11,]* m7[11,4]+w[12,]* m7[12,4]+w[13,]* m7[13,1]+w[14,]* m7[14,4]+w[15,]* m7[15,4]+w[16,]* m7[16,4]+w[17,]* m7[17,4]+w[18,]* m7[18,4]+w[19,]* m7[19,4]n[7,5] <- w[1,]* m7[1,5] + w[2,]* m7[2,5]+w[3,]* m7[3,5]+w[4,]* m7[4,5]+w[5,]* m7[5,5]+w[6,]* m7[6,5]+w[7,]* m7[7,5]+w[8,]* m7[8,5]+w[9,]* m7[9,5]+w[10,]* m7[10,5]+w[11,]* m7[11,5]+w[12,]* m7[12,5]+w[13,]* m7[13,1]+w[14,]* m7[14,5]+w[15,]* m7[15,5]+w[16,]* m7[16,5]+w[17,]* m7[17,5]+w[18,]* m7[18,5]+w[19,]* m7[19,5]
#计算2017年各元素值
n[8,1] <- w[1,]* m8[1,1] + w[2,]* m8[2,1]+w[3,]* m8[3,1]+w[4,]* m8[4,1]+w[5,]* m8[5,1]+w[6,]* m8[6,1]+w[7,]* m8[7,1]+w[8,]* m8[8,1]+w[9,]* m8[9,1]+w[10,]* m8[10,1]+w[11,]* m8[11,1]+w[12,]* m8[12,1]+w[13,]* m8[13,1]+w[14,]* m8[14,1]+w[15,]* m8[15,1]+w[16,]* m8[16,1]+w[17,]* m8[17,1]+w[18,]* m8[18,1]+w[19,]* m8[19,1]n[8,2] <- w[1,]* m8[1,2] + w[2,]* m8[2,2]+w[3,]* m8[3,2]+w[4,]* m8[4,2]+w[5,]* m8[5,2]+w[6,]* m8[6,2]+w[7,]* m8[7,2]+w[8,]* m8[8,2]+w[9,]* m8[9,2]+w[10,]* m8[10,2]+w[11,]* m8[11,2]+w[12,]* m8[12,2]+w[13,]* m8[13,2]+w[14,]* m8[14,2]+w[15,]* m8[15,2]+w[16,]* m8[16,2]+w[17,]* m8[17,2]+w[18,]* m8[18,2]+w[19,]* m8[19,2]n[8,3] <- w[1,]* m8[1,3] + w[2,]* m8[2,3]+w[3,]* m8[3,3]+w[4,]* m8[4,3]+w[5,]* m8[5,3]+w[6,]* m8[6,3]+w[7,]* m8[7,3]+w[8,]* m8[8,3]+w[9,]* m8[9,3]+w[10,]* m8[10,3]+w[11,]* m8[11,3]+w[12,]* m8[12,3]+w[13,]* m8[13,3]+w[14,]* m8[14,3]+w[15,]* m8[15,3]+w[16,]* m8[16,3]+w[17,]* m8[17,3]+w[18,]* m8[18,3]+w[19,]* m8[19,3]n[8,4] <- w[1,]* m8[1,4] + w[2,]* m8[2,4]+w[3,]* m8[3,4]+w[4,]* m8[4,4]+w[5,]* m8[5,4]+w[6,]* m8[6,4]+w[7,]* m8[7,4]+w[8,]* m8[8,4]+w[9,]* m8[9,4]+w[10,]* m8[10,4]+w[11,]* m8[11,4]+w[12,]* m8[12,4]+w[13,]* m8[13,1]+w[14,]* m8[14,4]+w[15,]* m8[15,4]+w[16,]* m8[16,4]+w[17,]* m8[17,4]+w[18,]* m8[18,4]+w[19,]* m8[19,4]n[8,5] <- w[1,]* m8[1,5] + w[2,]* m8[2,5]+w[3,]* m8[3,5]+w[4,]* m8[4,5]+w[5,]* m8[5,5]+w[6,]* m8[6,5]+w[7,]* m8[7,5]+w[8,]* m8[8,5]+w[9,]* m8[9,5]+w[10,]* m8[10,5]+w[11,]* m8[11,5]+w[12,]* m8[12,5]+w[13,]* m8[13,1]+w[14,]* m8[14,5]+w[15,]* m8[15,5]+w[16,]* m8[16,5]+w[17,]* m8[17,5]+w[18,]* m8[18,5]+w[19,]* m8[19,5]
#计算2018年各元素值
n[9,1] <- w[1,]* m9[1,1] + w[2,]* m9[2,1]+w[3,]* m9[3,1]+w[4,]* m9[4,1]+w[5,]* m9[5,1]+w[6,]* m9[6,1]+w[7,]* m9[7,1]+w[8,]* m9[8,1]+w[9,]* m9[9,1]+w[10,]* m9[10,1]+w[11,]* m9[11,1]+w[12,]* m9[12,1]+w[13,]* m9[13,1]+w[14,]* m9[14,1]+w[15,]* m9[15,1]+w[16,]* m9[16,1]+w[17,]* m9[17,1]+w[18,]* m9[18,1]+w[19,]* m9[19,1]n[9,2] <- w[1,]* m9[1,2] + w[2,]* m9[2,2]+w[3,]* m9[3,2]+w[4,]* m9[4,2]+w[5,]* m9[5,2]+w[6,]* m9[6,2]+w[7,]* m9[7,2]+w[8,]* m9[8,2]+w[9,]* m9[9,2]+w[10,]* m9[10,2]+w[11,]* m9[11,2]+w[12,]* m9[12,2]+w[13,]* m9[13,2]+w[14,]* m9[14,2]+w[15,]* m9[15,2]+w[16,]* m9[16,2]+w[17,]* m9[17,2]+w[18,]* m9[18,2]+w[19,]* m9[19,2]n[9,3] <- w[1,]* m9[1,3] + w[2,]* m9[2,3]+w[3,]* m9[3,3]+w[4,]* m9[4,3]+w[5,]* m9[5,3]+w[6,]* m9[6,3]+w[7,]* m9[7,3]+w[8,]* m9[8,3]+w[9,]* m9[9,3]+w[10,]* m9[10,3]+w[11,]* m9[11,3]+w[12,]* m9[12,3]+w[13,]* m9[13,3]+w[14,]* m9[14,3]+w[15,]* m9[15,3]+w[16,]* m9[16,3]+w[17,]* m9[17,3]+w[18,]* m9[18,3]+w[19,]* m9[19,3]n[9,4] <- w[1,]* m9[1,4] + w[2,]* m9[2,4]+w[3,]* m9[3,4]+w[4,]* m9[4,4]+w[5,]* m9[5,4]+w[6,]* m9[6,4]+w[7,]* m9[7,4]+w[8,]* m9[8,4]+w[9,]* m9[9,4]+w[10,]* m9[10,4]+w[11,]* m9[11,4]+w[12,]* m9[12,4]+w[13,]* m9[13,1]+w[14,]* m9[14,4]+w[15,]* m9[15,4]+w[16,]* m9[16,4]+w[17,]* m9[17,4]+w[18,]* m9[18,4]+w[19,]* m9[19,4]n[9,5] <- w[1,]* m9[1,5] + w[2,]* m9[2,5]+w[3,]* m9[3,5]+w[4,]* m9[4,5]+w[5,]* m9[5,5]+w[6,]* m9[6,5]+w[7,]* m9[7,5]+w[8,]* m9[8,5]+w[9,]* m9[9,5]+w[10,]* m9[10,5]+w[11,]* m9[11,5]+w[12,]* m9[12,5]+w[13,]* m9[13,1]+w[14,]* m9[14,5]+w[15,]* m9[15,5]+w[16,]* m9[16,5]+w[17,]* m9[17,5]+w[18,]* m9[18,5]+w[19,]* m9[19,5]
#计算2019年各元素值
n[10,1] <- w[1,]* m10[1,1] + w[2,]* m10[2,1]+w[3,]* m10[3,1]+w[4,]* m10[4,1]+w[5,]* m10[5,1]+w[6,]* m10[6,1]+w[7,]* m10[7,1]+w[8,]* m10[8,1]+w[9,]* m10[9,1]+w[10,]* m10[10,1]+w[11,]* m10[11,1]+w[12,]* m10[12,1]+w[13,]* m10[13,1]+w[14,]* m10[14,1]+w[15,]* m10[15,1]+w[16,]* m10[16,1]+w[17,]* m10[17,1]+w[18,]* m10[18,1]+w[19,]* m10[19,1]n[10,2] <- w[1,]* m10[1,2] + w[2,]* m10[2,2]+w[3,]* m10[3,2]+w[4,]* m10[4,2]+w[5,]* m10[5,2]+w[6,]* m10[6,2]+w[7,]* m10[7,2]+w[8,]* m10[8,2]+w[9,]* m10[9,2]+w[10,]* m10[10,2]+w[11,]* m10[11,2]+w[12,]* m10[12,2]+w[13,]* m10[13,2]+w[14,]* m10[14,2]+w[15,]* m10[15,2]+w[16,]* m10[16,2]+w[17,]* m10[17,2]+w[18,]* m10[18,2]+w[19,]* m10[19,2]n[10,3] <- w[1,]* m10[1,3] + w[2,]* m10[2,3]+w[3,]* m10[3,3]+w[4,]* m10[4,3]+w[5,]* m10[5,3]+w[6,]* m10[6,3]+w[7,]* m10[7,3]+w[8,]* m10[8,3]+w[9,]* m10[9,3]+w[10,]* m10[10,3]+w[11,]* m10[11,3]+w[12,]* m10[12,3]+w[13,]* m10[13,3]+w[14,]* m10[14,3]+w[15,]* m10[15,3]+w[16,]* m10[16,3]+w[17,]* m10[17,3]+w[18,]* m10[18,3]+w[19,]* m10[19,3]n[10,4] <- w[1,]* m10[1,4] + w[2,]* m10[2,4]+w[3,]* m10[3,4]+w[4,]* m10[4,4]+w[5,]* m10[5,4]+w[6,]* m10[6,4]+w[7,]* m10[7,4]+w[8,]* m10[8,4]+w[9,]* m10[9,4]+w[10,]* m10[10,4]+w[11,]* m10[11,4]+w[12,]* m10[12,4]+w[13,]* m10[13,1]+w[14,]* m10[14,4]+w[15,]* m10[15,4]+w[16,]* m10[16,4]+w[17,]* m10[17,4]+w[18,]* m10[18,4]+w[19,]* m10[19,4]n[10,5] <- w[1,]* m10[1,5] + w[2,]* m10[2,5]+w[3,]* m10[3,5]+w[4,]* m10[4,5]+w[5,]* m10[5,5]+w[6,]* m10[6,5]+w[7,]* m10[7,5]+w[8,]* m10[8,5]+w[9,]* m10[9,5]+w[10,]* m10[10,5]+w[11,]* m10[11,5]+w[12,]* m10[12,5]+w[13,]* m10[13,1]+w[14,]* m10[14,5]+w[15,]* m10[15,5]+w[16,]* m10[16,5]+w[17,]* m10[17,5]+w[18,]* m10[18,5]+w[19,]* m10[19,5]
#计算2020年各元素值
n[11,1] <- w[1,]* m11[1,1] + w[2,]* m11[2,1]+w[3,]* m11[3,1]+w[4,]* m11[4,1]+w[5,]* m11[5,1]+w[6,]* m11[6,1]+w[7,]* m11[7,1]+w[8,]* m11[8,1]+w[9,]* m11[9,1]+w[10,]* m11[10,1]+w[11,]* m11[11,1]+w[12,]* m11[12,1]+w[13,]* m11[13,1]+w[14,]* m11[14,1]+w[15,]* m11[15,1]+w[16,]* m11[16,1]+w[17,]* m11[17,1]+w[18,]* m11[18,1]+w[19,]* m11[19,1]n[11,2] <- w[1,]* m11[1,2] + w[2,]* m11[2,2]+w[3,]* m11[3,2]+w[4,]* m11[4,2]+w[5,]* m11[5,2]+w[6,]* m11[6,2]+w[7,]* m11[7,2]+w[8,]* m11[8,2]+w[9,]* m11[9,2]+w[10,]* m11[10,2]+w[11,]* m11[11,2]+w[12,]* m11[12,2]+w[13,]* m11[13,2]+w[14,]* m11[14,2]+w[15,]* m11[15,2]+w[16,]* m11[16,2]+w[17,]* m11[17,2]+w[18,]* m11[18,2]+w[19,]* m11[19,2]n[11,3] <- w[1,]* m11[1,3] + w[2,]* m11[2,3]+w[3,]* m11[3,3]+w[4,]* m11[4,3]+w[5,]* m11[5,3]+w[6,]* m11[6,3]+w[7,]* m11[7,3]+w[8,]* m11[8,3]+w[9,]* m11[9,3]+w[10,]* m11[10,3]+w[11,]* m11[11,3]+w[12,]* m11[12,3]+w[13,]* m11[13,3]+w[14,]* m11[14,3]+w[15,]* m11[15,3]+w[16,]* m11[16,3]+w[17,]* m11[17,3]+w[18,]* m11[18,3]+w[19,]* m11[19,3]n[11,4] <- w[1,]* m11[1,4] + w[2,]* m11[2,4]+w[3,]* m11[3,4]+w[4,]* m11[4,4]+w[5,]* m11[5,4]+w[6,]* m11[6,4]+w[7,]* m11[7,4]+w[8,]* m11[8,4]+w[9,]* m11[9,4]+w[10,]* m11[10,4]+w[11,]* m11[11,4]+w[12,]* m11[12,4]+w[13,]* m11[13,1]+w[14,]* m11[14,4]+w[15,]* m11[15,4]+w[16,]* m11[16,4]+w[17,]* m11[17,4]+w[18,]* m11[18,4]+w[19,]* m11[19,4]n[11,5] <- w[1,]* m11[1,5] + w[2,]* m11[2,5]+w[3,]* m11[3,5]+w[4,]* m11[4,5]+w[5,]* m11[5,5]+w[6,]* m11[6,5]+w[7,]* m11[7,5]+w[8,]* m11[8,5]+w[9,]* m11[9,5]+w[10,]* m11[10,5]+w[11,]* m11[11,5]+w[12,]* m11[12,5]+w[13,]* m11[13,1]+w[14,]* m11[14,5]+w[15,]* m11[15,5]+w[16,]* m11[16,5]+w[17,]* m11[17,5]+w[18,]* m11[18,5]+w[19,]* m11[19,5]
#查看生成的多指标综合测度矩阵
n <- as.data.frame(n)
write.csv(n,file='多指标综合测度矩阵.csv')第四步，计算各个样本的得分
#计算各个样本的得分
q1 <- n[1,1]*5+n[1,2]*4+n[1,3]*3+n[1,4]*2+n[1,5]*1
q2 <- n[2,1]*5+n[2,2]*4+n[2,3]*3+n[2,4]*2+n[2,5]*1
q3 <- n[3,1]*5+n[3,2]*4+n[3,3]*3+n[3,4]*2+n[3,5]*1
q4 <- n[4,1]*5+n[4,2]*4+n[4,3]*3+n[4,4]*2+n[4,5]*1
q5 <- n[5,1]*5+n[5,2]*4+n[5,3]*3+n[5,4]*2+n[5,5]*1
q6 <- n[6,1]*5+n[6,2]*4+n[6,3]*3+n[6,4]*2+n[6,5]*1
q7 <- n[7,1]*5+n[7,2]*4+n[7,3]*3+n[7,4]*2+n[7,5]*1
q8 <- n[8,1]*5+n[8,2]*4+n[8,3]*3+n[8,4]*2+n[8,5]*1
q9 <- n[9,1]*5+n[9,2]*4+n[9,3]*3+n[9,4]*2+n[9,5]*1
q10 <- n[10,1]*5+n[10,2]*4+n[10,3]*3+n[10,4]*2+n[10,5]*1
q11 <- n[11,1]*5+n[11,2]*4+n[11,3]*3+n[11,4]*2+n[11,5]*1
qt <- c(q1,q2,q3,q4,q5,q6,q7,q8,q9,q10,q11)
write.csv(qt,file='得分.csv')本文来自互联网用户投稿，文章观点仅代表作者本人，不代表本站立场，不承担相关法律责任。如若转载，请注明出处。 如若内容造成侵权/违法违规/事实不符，请点击【内容举报】进行投诉反馈！