SAS Logistic回归程序代码和输出结果.docx
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SAS Logistic回归程序代码和输出结果.docx
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SASLogistic回归程序代码和输出结果
[SAS]Logistic回归程序代码和输出结果
基于贝叶斯判别的房地产信用评级研究
本文首先采用Logistic回归法筛选出4个财务指标作为评价函数的计量参数,
再构造Bayes判别算法建立信用评估模型,
将其应用于某些房地产企业的实际数据分析,并评估其评判效果。
程序代码
dataLOGIT;inputgx1-x10@@; /* 输入数据和对应的变量名称,指定数据是按顺序对应变量(@@) */
cards;
1 76.02 112.1652.65 16.24 4.17 88.54 -1.93 98.07 -58.63-1.93
1 50.15 53.55 6.18 5.81 0.77 6.91 5.89 105.8918.21 5.89
1 35.94 8.04 0.25 12.89 0.04 11.54 0.25 100.253.56 0.25
2 36.03 65.44 5.07 4.71 0.77 -4.21 2.42 102.4247.27 2.42
2 76.95 86.32 -6.38 14.28 -0.51 101.50-6.18 93.82 34.19 -6.18
2 36.36 37.91 6.01 10.78 0.87 -11.036.20 106.2043.43 6.20
2 45.44 46.41 -1.09 14.04 -0.14 82.45 130.53230.53-82.56130.53
2 48.80 43.19 6.97 11.15 0.94 20.58 8.62 108.627.67 8.62
2 21.09 45.85 6.10 13.79 0.00 32.70 6.86 106.86-91.486.86
2 26.38 1.14 16.25 7.98 2.26 -31.8315.26 115.2663.42 15.26
2 32.61 26.18 8.51 22.08 1.45 10.71 8.89 108.896.14 8.89
2 25.16 57.63 20.94 23.88 3.44 -0.98 30.46 130.4660.45 30.46
2 48.47 39.56 8.23 10.76 1.06 7.67 8.56 108.5645.65 8.56
3 52.05 75.95 24.12 13.18 2.50 -7.47 24.90 124.9018.17 24.90
3 86.92 14.00 4.55 10.96 0.38 -23.56-79.8320.17 36.01 -79.83
3 39.96 41.87 7.10 12.04 -0.12 8.20 3.24 103.245.98 3.24
1 65.00 29.00 1.50 2.00 0.16 54.55 -0.63 99.37 -58.34-0.63
2 66.20 30.52 21.51 23.18 1.77 16.29 23.42 123.4231.15 23.42
…………
;
proclogisticdata=LOGITdes; /* 选择Logistic回归模型对这个数据进行分析,对因变量设置des概率 */
modelg=x1x2x3x4 x5x6x7x8x9x10/selection=stepwiseslentry=0.15slstay=0.15; /* 指定因变量和自变量,
逐步选择变量,设置stepwise显著性水平0.15*/
run;
输出结果
SAS系统 2012年05月26日星期六下午12时31分22秒 1
TheLOGISTICProcedure
ModelInformation
DataSet WORK.LOGIT
ResponseVariable g
NumberofResponseLevels 3
Model cumulativelogit
OptimizationTechnique Fisher'sscoring
NumberofObservationsRead 48
NumberofObservationsUsed 48
ResponseProfile
Ordered Total
Value g Frequency
1 3 13
2 2 31
3 1 4
ProbabilitiesmodeledarecumulatedoverthelowerOrderedValues.
StepwiseSelectionProcedure
Step 0.Interceptsentered:
ModelConvergenceStatus
Convergencecriterion(GCONV=1E-8)satisfied.
-2LogL=80.949
ResidualChi-SquareTest
Chi-Square DF Pr>ChiSq
13.0922 8 0.1087
NOTE:
No(additional)effectsmetthe0.05significancelevelforentryintothemodel.
AnalysisofMaximumLikelihoodEstimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr>ChiSq
Intercept3 1 -0.9904 0.3248 9.2980 0.0023
Intercept2 1 2.3979 0.5222 21.0830 <.0001
SAS系统 2012年05月26日星期六下午12时31分22秒 2
TheLOGISTICProcedure
ModelInformation
DataSet WORK.LOGIT
ResponseVariable g
NumberofResponseLevels 3
Model cumulativelogit
OptimizationTechnique Fisher'sscoring
NumberofObservationsRead 48
NumberofObservationsUsed 48
ResponseProfile
Ordered Total
Value g Frequency
1 3 13
2 2 31
3 1 4
ProbabilitiesmodeledarecumulatedoverthelowerOrderedValues.
StepwiseSelectionProcedure
Step 0.Interceptsentered:
ModelConvergenceStatus
Convergencecriterion(GCONV=1E-8)satisfied.
-2LogL=80.949
ResidualChi-SquareTest
Chi-Square DF Pr>ChiSq
13.0922 8 0.1087
Step 1.Effectx4entered:
ModelConvergenceStatus
Convergencecriterion(GCONV=1E-8)satisfied.
ScoreTestfortheProportionalOddsAssumption
Chi-Square DF Pr>ChiSq
4.7698 1 0.0290
SAS系统 2012年05月26日星期六下午12时31分22秒 3
TheLOGISTICProcedure
ModelFitStatistics
Intercept
Intercept and
Criterion Only Covariates
AIC 84.949 83.246
SC 88.691 88.859
-2LogL 80.949 77.246
TestingGlobalNullHypothesis:
BETA=0
Test Chi-Square DF Pr>ChiSq
LikelihoodRatio 3.7032 1 0.0543
Score 3.7112 1 0.0540
Wald 3.2133 1 0.0730
ResidualChi-SquareTest
Chi-Square DF Pr>ChiSq
10.0282 7 0.1870
NOTE:
NoeffectsforthemodelinStep1areremoved.
Step 2.Effectx6entered:
ModelConvergenceStatus
Convergencecriterion(GCONV=1E-8)satisfied.
ScoreTestfortheProportionalOddsAssumption
Chi-Square DF Pr>ChiSq
5.0078 2 0.0818
ModelFitStatistics
Intercept
Intercept and
Criterion Only Covariates
AIC 84.949 81.703
SC 88.691 89.187
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