##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, nrowiff[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)