Regression logistique sortie sas et aquation du modele
bonjour
je voudrais des améliorations pour ce code sas, graphique de la courbe de roc et aussi l’écriture du modèle avec cette sortie
code :
Citation:
proc logistic data=memoire.avc_estimation desc;
class Age(ref="3") delais_Ps_Ad(ref="5") cli_Tr_Conscience(ref="1") graviteSigne_Grav_Hemorag_Iv(ref="1")
Type_AVC(ref="1") gravite_Engagement(ref="1") /param=ref;
model pronostic = Age delais_Ps_Ad cli_Tr_Conscience graviteSigne_Grav_Hemorag_Iv
Type_AVC gravite_Engagement;
run;
voici la sortie de sas : la variable explicative est pronostic codée 1=normal; 2=AVCI;3=AVCH
Code:
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| The SAS System 10:23 Thursday, January 15, 2021 1
The LOGISTIC Procedure
Model Information
Data Set MEMOIRE.AVC_ESTIMATION
Response Variable Pronostic Pronostic
Number of Response Levels 2
Model binary logit
Optimization Technique Fisher's scoring
Number of Observations Read 114
Number of Observations Used 114
Response Profile
Ordered Total
Value Pronostic Frequency
1 2 84
2 1 30
Probability modeled is Pronostic=2.
Class Level Information
Class Value Design Variables
Age 1 1 0
2 0 1
3 0 0
delais_Ps_Ad 1 1 0 0 0
2 0 1 0 0
3 0 0 1 0
4 0 0 0 1
5 0 0 0 0
cli_Tr_Conscience 1 0
2 1
graviteSigne_Grav_Hemorag_Iv 1 0
2 1
Type_AVC 1 0 0
2 1 0
3 0 1
gravite_Engagement 1 0
The SAS System 10:23 Thursday, January 15, 2021 2
The LOGISTIC Procedure
Class Level Information
Class Value Design Variables
2 1
Model Convergence Status
Quasi-complete separation of data points detected.
WARNING: The maximum likelihood estimate may not exist.
WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are
based on the last maximum likelihood iteration. Validity of the model fit is
questionable.
Model Fit Statistics
Intercept
Intercept and
Criterion Only Covariates
AIC 133.404 90.014
SC 136.140 122.848
-2 Log L 131.404 66.014
Testing Global Null Hypothesis: BETA=0
Test Chi-Square DF Pr > ChiSq
Likelihood Ratio 65.3907 11 <.0001
Score 47.2922 11 <.0001
Wald 20.7771 11 0.0358
Type 3 Analysis of Effects
Wald
Effect DF Chi-Square Pr > ChiSq
Age 2 0.4505 0.7983
delais_Ps_Ad 4 11.7118 0.0196
cli_Tr_Conscience 1 4.6727 0.0306
graviteSigne_Grav_He 1 7.8040 0.0052
Type_AVC 2 2.5890 0.2740
gravite_Engagement 1 5.4565 0.0195
The SAS System 10:23 Thursday, January 15, 2021 3
The LOGISTIC Procedure
WARNING: The validity of the model fit is questionable.
Analysis of Maximum Likelihood Estimates
Standard Wald
Parameter DF Estimate Error Chi-Square Pr > ChiSq
Intercept 1 13.2538 154.3 0.0074 0.9316
Age 1 1 -0.4091 1.1861 0.1190 0.7301
Age 2 1 -0.4947 0.7602 0.4234 0.5152
delais_Ps_Ad 1 1 3.5299 1.4407 6.0033 0.0143
delais_Ps_Ad 2 1 -0.4385 0.9153 0.2295 0.6319
delais_Ps_Ad 3 1 -0.9602 1.0582 0.8234 0.3642
delais_Ps_Ad 4 1 -2.5469 1.4508 3.0820 0.0792
cli_Tr_Conscience 2 1 1.4780 0.6838 4.6727 0.0306
graviteSigne_Grav_He 2 1 2.7308 0.9775 7.8040 0.0052
Type_AVC 2 1 -13.6021 154.3 0.0078 0.9298
Type_AVC 3 1 -15.1685 154.3 0.0097 0.9217
gravite_Engagement 2 1 2.0779 0.8895 5.4565 0.0195
Odds Ratio Estimates
Point 95% Wald
Effect Estimate Confidence Limits
Age 1 vs 3 0.664 0.065 6.791
Age 2 vs 3 0.610 0.137 2.705
delais_Ps_Ad 1 vs 5 34.122 2.026 574.593
delais_Ps_Ad 2 vs 5 0.645 0.107 3.878
delais_Ps_Ad 3 vs 5 0.383 0.048 3.046
delais_Ps_Ad 4 vs 5 0.078 0.005 1.345
cli_Tr_Conscience 2 vs 1 4.384 1.148 16.747
graviteSigne_Grav_He 2 vs 1 15.345 2.259 104.244
Type_AVC 2 vs 1 <0.001 <0.001 >999.999
Type_AVC 3 vs 1 <0.001 <0.001 >999.999
gravite_Engagement 2 vs 1 7.987 1.397 45.663
Association of Predicted Probabilities and Observed Responses
Percent Concordant 92.7 Somers' D 0.860
Percent Discordant 6.7 Gamma 0.866
Percent Tied 0.7 Tau-a 0.336
Pairs 2520 c 0.930 |
vous m'excuserez de la mauvaise présentation de la discussion, c'est mon premier
