LOGISTIC回归及SAS程序很全哦Word下载.docx
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Freqcount;
Classdrug/param=refdescending;
Modelresponse=drugdegree/rsqscale=naggregate;
Run;
Rsq显示R2
Scale,SCALE=specifiesmethodtocorrectoverdispersion,指定参数，=n表示不需要修正。
Aggregate计算卡方检验统计量
Class语句将分类变量化成虚拟变量，三种药用两个虚拟变量表示。
TheLOGISTICProcedure
ModelInformation
DataSetWORK.EX12_1
ResponseVariableresponse
NumberofResponseLevels2
FrequencyVariablecount
Modelbinarylogit
OptimizationTechniqueFisher'
sscoring
NumberofObservationsRead12
NumberofObservationsUsed12
SumofFrequenciesRead577
SumofFrequenciesUsed577
ResponseProfile
OrderedTotal
ValueresponseFrequency
11315
20262
Probabilitymodeledisresponse=1.
ClassLevelInformation
Design
ClassValueVariables
drug210
101
000
ModelConvergenceStatus
Convergencecriterion（GCONV=1E-8）satisfied.
DevianceandPearsonGoodness-of-FitStatistics
CriterionValueDFValue/DFPr>
ChiSq
Deviance0.374920.18740.8291
Pearson0.368920.18440.8316
模型拟合集优度检验，
Numberofuniqueprofiles:
6
ModelFitStatistics
Intercept
Interceptand
CriterionOnlyCovariates
AIC797.017641.326
SC801.375658.757
-2LogL795.017633.326
R-Square0.2444Max-rescaledR-Square0.3268
TestingGlobalNullHypothesis:
BETA=0
TestChi-SquareDFPr>
LikelihoodRatio161.69073<
.0001
Score148.15983<
Wald118.13943<
检验模型全部系数为0，拒绝则模型有意义
Type3AnalysisofEffects
Wald
EffectDFChi-SquarePr>
drug295.0859<
degree147.4607<
AnalysisofMaximumLikelihoodEstimates
StandardWald
ParameterDFEstimateErrorChi-SquarePr>
Intercept1-1.95940.222977.2441<
drug211.83420.240658.0936<
drug112.28500.247984.9472<
degree11.38060.200447.4607<
参数估计与检验
OddsRatioEstimates
Point95%Wald
EffectEstimateConfidenceLimits
drug2vs06.2603.90610.033
drug1vs09.8266.04415.974
degree3.9772.6855.891
AssociationofPredictedProbabilitiesandObservedResponses
PercentConcordant72.2Somers'
D0.568
PercentDiscordant15.4Gamma0.649
PercentTied12.4Tau-a0.282
Pairs82530c0.784
铸铁冶炼，要对铁加热heat和水中热处理（soakingtime），n表示铸铁块数，r表示没有准备好轧制的铁块数。
dataingots;
inputHeatSoakrn@@;
datalines;
71.0010141.0031271.0156511.0313
71.7017141.7043271.7444511.701
72.207142.2233272.2021512.201
72.8012142.8031272.8122514.001
74.009144.0019274.0116
;
proclogisticdata=ingots;
modelr/n=HeatSoak;
run;
TheLOGISTICProcedure
ModelInformation
DataSetWORK.INGOTS
ResponseVariable（Events）r
ResponseVariable（Trials）n
Modelbinarylogit
OptimizationTechniqueFisher'
实验次数n,事件发生次数r
NumberofObservationsRead19
NumberofObservationsUsed19
SumofFrequenciesRead387
SumofFrequenciesUsed387
ResponseProfile
OrderedBinaryTotal
ValueOutcomeFrequency
1Event12
2Nonevent375
响应变量分析，发生12次，不发生375次。
ModelConvergenceStatus
Convergencecriterion（GCONV=1E-8）satisfied.
ModelFitStatistics
CriterionOnlyCovariates
AIC108.988101.346
SC112.947113.221
-2LogL106.98895.346
用于选择最优级模型，越小越优级
TestingGlobalNullHypothesis:
TestChi-SquareDFPr>
LikelihoodRatio11.642820.0030
Score15.109120.0005
Wald13.031520.0015
模型检验
似然比检验（likelihoodratiotest）、计分检验（scoretest）、Wald检验（Waldtest）三种
AnalysisofMaximumLikelihoodEstimates
StandardWald
Intercept1-5.55921.119724.6503<
Heat10.08200.023711.94540.0005
Soak10.05680.33120.02940.8639
系数检验
OddsRatioEstimates
Point95%Wald
EffectEstimateConfidenceLimits
Heat1.0851.0361.137
Soak1.0580.5532.026
AssociationofPredictedProbabilitiesandObservedResponses
PercentConcordant64.4Somers'
D0.460
PercentDiscordant18.4Gamma0.555
PercentTied17.2Tau-a0.028
Pairs4500c0.730
Usingtheparameterestimates,youcancalculatetheestimatedlogitofas
Logit（p）=log（p/1-p）=-5.5592+0.082×
Heat+0.0568×
Soak
IfHeat=7andSoak=1,thenlogit（p）=-4.92584.Usingthislogitestimate,youcancalculateasfollows:
P=1/（1+e4.9284）=0.0072
Y表示骑车上班（Y=1bike,Y=0,BUS）,X1年龄,X2月收入,X3性别（1男,0女）
X3
X1
X2
y
18
850
21
1200
23
1
950
28
31
36
1500
42
1000
46
48
55
1800
56
2100
58
20
25
27
1300
30
32
33
38
41
45
52
Datap256;
InputX3X1X2y;
0188500
02112000
0238501
0239501
02812001
0318500
03615001
04210001
0469501
04812000
05518001
05621001
05818001
1188500
12010000
12512000
12713000
12815000
1309501
13210000
13318000
13310000
13812000
14115000
14518001
14810000
15215001
15618001
Proclogisticdata=p256descending;
Modely=x1-x3;
outputout=predp=phatlower=lclupper=ucl
predprobs=（individualcrossvalidate）;
procprintdata=pred;
DataSetWORK.P256
ResponseVariabley
NumberofResponseLevels2
NumberofObservationsRead28
NumberofObservationsUsed28
OrderedTotal
ValueyFrequency
1015
2113
Probabilitymodeledisy=0.
AIC40.67333.971
SC42.00539.299
-2LogL38.67325.971
LikelihoodRatio12.702630.0053
Score10.413530.0154
Wald6.533130.0884
Intercept13.65472.09113.05450.0805
X11-0.08220.05212.48530.1149
X21-0.001520.001870.66130.4161
X312.50161.15784.66890.0307
X10.9210.8321.020
X20.9980.9951.002
X312.2031.262118.014
PercentConcordant87.2Somers'
D0.744
PercentDiscordant12.8Gamma0.744
PercentTied0.0Tau-a0.384
Pairs195c0.872
序号
样品数W
其中有房屋数
收入（千元）
10.0
1.5
2.0
2
20.0
3.2
3.0
3
25.0
4.0
4
30.0
5.0
5
40.0
8.0
6.0
6
50.0
12.0
7
60.0
18.0
8
80.0
28.0
13.0
9
100.0
45.0
15.0
10
70.0
36.0
11
65.0
39.0
12
33.0
13
35.0
14
15
27.0
16
38.0
17
48.0
58.0
Dataex1;
Inputnonn1x;
110.01.52.0
220.03.23.0
325.04.04.0
430.05.05.0
540.08.06.0
650.012.08.0
760.018.010.0
880.028.013.0
9100.045.015.0
1070.036.020.0
1165.039.025.0
1250.033.030.0
1340.030.035.0
1425.020.040.0
1530.027.050.0
1640.038.060.0
1750.048.070.0
1860.058.080.0
Proclogisticdata=ex1;
Modeln1/n=x;
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