基于多维标度和聚类的CPI数据结构分析
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摘要
基于3种距离结构应用多维标度法和Ward聚类法对2016年我国各地区的CPI数据进行分析.利用R软件分析得到Stress值、低维空间匹配图、聚类树图及组合MDS和聚类结果图,研究了8个指标之间的潜在关系.结果表明:(1)医疗保健和家庭设备用品及服务分别与其他6个指标具有较弱的相关性,它们各自构成一类;(2)娱乐教育文化可以与交通和通信、烟酒及用品、衣着分为一类,也可以将娱乐教育文化与居住、食品分为一类.
Based on three distance structures, the CPI data of various regions in China in 2016 were analyzed by multi-dimensional scaling method and Ward clustering method. Stress value, low-dimensional spatial matching graph, clustering tree graph, combined MDS and clustering result graph were obtained by R software analysis. Potential relationships among the eight indicators were studied. The results showed that:(1) Health care and household equipment supplies and services have weak correlation with the other six indicators, and they each constitute one cluster;(2) Entertainment education culture can be divided into one category of transportation and communication, tobacco and alcohol supplies and clothing, and can also be classified into residential and food categories.
Based on three distance structures, the CPI data of various regions in China in 2016 were analyzed by multi-dimensional scaling method and Ward clustering method. Stress value, low-dimensional spatial matching graph, clustering tree graph, combined MDS and clustering result graph were obtained by R software analysis. Potential relationships among the eight indicators were studied. The results showed that:(1) Health care and household equipment supplies and services have weak correlation with the other six indicators, and they each constitute one cluster;(2) Entertainment education culture can be divided into one category of transportation and communication, tobacco and alcohol supplies and clothing, and can also be classified into residential and food categories.
引文
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[2] 王君美.城市与农村消费价格指数的动态关联实证分析[J].统计与决策,2010,310(10):86-88.
[3] 张菊花,魏立力.三角模糊数多维标度分析及其应用[J].统计与决策,2014,414(18):28-31.
[4] 汤银才.R语言与统计分析[M].北京:高等教育出版社,2008.
[5] 刘金焕.居民消费价格指数预测的方法探讨[J].统计与决策,2014,399(3):82-84.
[6] BORY,INGWER G,PUTICK J F.Groenen.Modern Multidimensional Scaling[M].Berlin:Springer Verlay,2005.
[7] 平卫英.我国区域CPI波动差异及其影响因素分析[J].价格理论与实践,2015,378(12):92-94.
[8] 揭水平.多维标度法的聚类分析:问题与解法[J].统计与决策,2009,287(11):148-149.
[9] 宁夏统计局.宁夏统计年鉴2017[M].北京:中国统计出版社,2018.