SAS变量聚类过程

1、SAS变量聚类过程13个上市公司的财务状况指标Num：股票代码X1-流动比率X1-速动比率X1-总资产周转率X1-存货周转率X1-营运资本X1-每股收益X1-净利润增长率X1-每股收益增长率X1-主营业务毛利率X1-主营业务利润率X1-成本费用利润率X1-净资产收益率X1-总资产利润率一、变量聚类过程代码Proc varclus data=a maxclusters=3Var x1-x13;Run;二、输出结果Observations48 Proportion0Variables13 Maxeigen1Cluster Variation ProportionSecondClusterMembe

2、rsVariation ExplainedExplainedEigenvalue113136.6438310.51112.3926Total variation explained = 6.643831 Proportion = 0.5111Cluster 1 will be split because it has the largest second eigenvalue, 2.392611, which is greater than the MAXEIGEN=1 value.Clustering algorithm converged.ClusterMembersCluster Sum

3、mary for 2 ClustersClusterVariationVariationExplainedProportionExplainedSecondEigenvalue110106.19720.61971.48382332.675480.89180.2719Total variation explained = 8.872681 Proportion = 0.6825R-squared withClustersOwn Next 1-R*2 VariableClusterVariableClusterClosestRatio LabelCluster 1X30.38760.00460.6

4、152X3X40.34740.00030.6527X4X60.88430.08790.1269X6X70.47350.05160.5551X7X80.47040.05440.5601X8X90.28170.03290.7428X9X100.65310.08290.3782X10X110.79580.13470.2360X11X120.97070.10290.0327X12X130.93270.12010.0765X13Cluster 2X10.83480.02100.1688X1X20.96360.14440.0425X2X50.87710.13040.1414X5Oblique Princi

5、pal Component Cluster AnalysisStandardized Scoring CoefficientsCluster12X1X10.0000000.341495X2X20.0000000.366908X3X30.1004650.000000X4X40.0951130.000000X5X50.0000000.350036X6X60.1517400.000000X7X70.1110390.000000X8X80.1106750.000000X9X90.0856390.000000X10X100.1304070.000000X11X110.1439490.000000X12X

6、120.1589780.000000X13X130.1558350.000000Cluster StructureCluster12X1X10.1448290.913662X2X20.3799400.981654X3X30.6226030.067735X4X40.5894330.015849X5X50.3610710.936513X6X60.9403610.296508X7X70.6881330.227193X8X80.6858720.233311X9X90.5307240.181441X10X100.8081590.287862X11X110.8920830.367010X12X120.98

7、52170.320739X13X130.9657400.346498Inter-Cluster Correlations1.000000.315250.315251.00000Cluster 1 will be split because it has the largest second eigenvalue, 1.483798, which is greater than the MAXEIGEN=1 value.Clustering algorithm converged.Oblique Principal Component Cluster AnalysisCluster Summar

8、y for 3 ClustersClusterMembersClusterVariationVariationExplainedProportionExplainedSecondEigenvalue1664.8430740.80720.66142332.675480.89180.27193442.5054570.62641.1988Total variation explained = 10.02401 Proportion = 0.7711R-squared withClustersOwn Next 1-R*2 VariableCluster Variable Cluster Closest

9、 Ratio LabelCluster 1X60.85710.51770.2963X6X90.43410.03290.5852X9X100.79190.17520.2523X10X110.87450.32740.1866X11X120.94630.56810.1242X12X130.93910.49420.1203X13Cluster 2X10.83480.03280.1708X1X20.96360.16190.0434X2X50.87710.13660.1424X5Cluster 3X30.53600.23930.6100X3X40.51420.20670.6124X4X70.72180.2

10、8730.3903X7X80.73350.28140.3709X8Standardized Scoring CoefficientsCluster123,X1X10.0000000.3414950.000000X2X20.0000000.3669080.000000X3X30.0000000.0000000.292209X4X40.0000000.0000000.286199X5X50.0000000.3500360.000000X6X60.1911580.0000000.000000X7X70.0000000.0000000.339099X8X80.0000000.0000000.34182

11、5X9X90.1360420.0000000.000000X10X100.1837470.0000000.000000X11X110.1930900.0000000.000000X12X120.2008640.0000000.000000X13X130.2000970.0000000.000000Oblique Principal Component Cluster AnalysisCluster StructureCluster123X1X10.1811800.9136620.027008X2X20.4024160.9816540.232536X3X30.4892140.0677350.73

12、2118X4X40.4545920.0158490.717059X5X50.3695620.9365130.247344X6X60.9257910.2965080.719533X7X70.5359750.2271930.849599X8X80.5304280.2333110.856428X9X90.6588620.1814410.140588X10X100.8898990.2878620.418592X11X110.9351490.3670100.572153X12X120.9727990.3207390.753720X13X130.9690840.3464980.702967Inter-Cl

13、uster CorrelationsCluster12311.000000.338880.6361220.338881.000000.1811230.636120.181121.00000Cluster 3 will be split because it has the largest second eigenvalue, 1.198797, which is greater than the MAXEIGEN=1 value.Clustering algorithm converged.Cluster Summary for 4 ClustersClusterMembersClusterV

14、ariationVariationExplainedProportionExplainedSecondEigenvalue-1664.843074-0.80720.66142332.675480.89180.27193221.9575620.97880.04244221.7463230.87320.2537Total variation explained = 11.22244 Proportion = 0.8633Oblique Principal Component Cluster AnalysisR-squared withClustersOwn Next 1-R*2 VariableC

15、luster Variable Cluster Closest Ratio LabelCluster 1X60.85710.36170.2239X6X90.43410.04500.5926X9X100.79190.13050.2393X10X110.87450.25390.1682X11X120.94630.40380.0900X12X130.93910.36060.0952X13Cluster 2X10.83480.03280.1708X1X20.96360.16190.0434X2X50.87710.13660.1424X5Cluster 3X70.97880.28730.0298X7X8

16、0.97880.28140.0295X8Cluster 4X30.87320.23930.1667X3X40.87320.20670.1599X4Standardized Scoring CoefficientsCluster1234X1X10.0000000.341495-0.0000000.000000X2X20.0000000.3669080.0000000.000000X3X30.0000000.0000000.0000000.535085X4X40.0000000.0000000.0000000.535085X5X50.0000000.3500360.0000000.000000X6

17、X60.1911580.0000000.0000000.000000X7X70.0000000.0000000.5053910.000000X8X80.0000000.0000000.5053910.000000X9X90.1360420.0000000.0000000.000000X10X100.1837470.0000000.0000000.000000X11X110.1930900.0000000.0000000.000000X12X120.2008640.0000000.0000000.000000X13X130.2000970.0000000.0000000.000000Obliqu

18、e Principal Component Cluster AnalysisCluster StructureCluster1234X1X10.1811800.9136620.133759-.117476X2X20.4024160.9816540.2649180.099914X3X30.4892140.0677350.3359220.934431X4X40.4545920.0158490.3158260.934431X5X50.3695620.9365130.2567050.137651X6X60.9257910.2965080.5853060.601407X7X70.5359750.2271930.9893340.338802X8X80.5304280.2333110.9893340.351240X9X90.6588620.1814410.212141-.004224X10X100.8898990.2878620.3314660.361294X11X110.9351490.3670100.5038370.430502X12X120.9727990.3207390.6354660.602190X13X130.969084