R语言使用灰色关联分析(Grey Relation Analysis,GRA)中国经济社会发展指标

##生成数据


#' economyCompare = data.frame(refer, liaoning, shandong, jiangsu, zhejiang, fujian, guangdong)


#
# 异常控制 #
if (any(is.na(df))) stop("'df' have NA" )
if (distingCoeff<0 | distingCoeff>1) stop("'distingCoeff' must be in range of [0,1]" )








diff = X  #设置差学列矩阵空间


for (i in
mx = max(diff)




#计算关联系数#
relations = (mi+distingCoeff*mx) / (diff + distingCoeff*mx)


#计算关联度#
# 暂时简单处理, 等权
relDegree = rep(NA, nc)
for (i in 1:nc) {
relDegree[i] = mean(relations[,i])  # 等权
}




#排序: 按关联度大到小#
X_order = X[order(relDegree,
relDes = rep(NA, nc) #分配空间  关联关系描述(说明谁和谁的关联度)
X_names = names(X_o
names(relationalDegree) = relDes




if (cluster) {


greyRelDegree = GRA(economyC




# 得到差异率矩阵 #
grey_diff = matrix(0


grey_diff[i,j] = abs(rel
#得到距离矩阵#
grey_dist = matrix(0, nrow
iff[i,j]+grey_diff[j,i]
}
}


# 得到灰色相关系数矩阵 #
grey_dist_max = max(grey_dist)
grey_correl = matrix(0, nrow = nc, ncol = nc)
for (i in 1:nc) {
for (j in 1:nc) {
grey_correl[i,j] = 1 - grey_dist[i,j] / grey_dist_max
}
}






d = as.dist(1-grey_correl)  # 得到无对角线的下三角矩阵(数值意义反向了, 值越小表示越相关 )
# 主对角线其实表示了各个对象的相近程度, 画图的时候, 相近的对象放在一起


hc = hclust(d, method = clusterMethod)  # 系统聚类(分层聚类)函数, single: 单一连接(最短距离法/最近邻)
# hc$height, 是上面矩阵的对角元素升序
# hc$order, 层次树图上横轴个体序号
plot(hc,hang=-1)  #hang: 设置标签悬挂位置


}


#输出#


if (cluster)  {
lst = list(relationalDegree=relationalDegree,


return(lst)


}
```
```{r}
## 生成数据
rownames(economyCompare) = c("indGV", "indVA", "profit", "incomeTax")
## 灰色关联度
greyRelDegree = greya(economyCompare)
greyRelDegree
```

## 灰色聚类


greya(economyCompare, cluster = T)