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I am having difficulty reconciling the differences in the odds ratios
from the same data set while performing a logistic regression with JMP
(v5) vs. SAS (v9). When the variable is categorical with only two response levels both JMP and SAS calculate the same odds ratios. When the categorical variable has 3 or more response levels the ORs differ between JMP and SAS. Changing to reference coding from effects in SAS doesn't change the ORs (the estimates do change). I'm not sure how JMP is coding the dummy variables and why the outputs differ. Following is a summary of my results: Model: female = ses read. Female (0/1, probability modeled is female=0). ses (three responses: 1, 2, 3). Read is a continuous variable (min value 28, max value 76). JMP Logistic Regression Analysis Intercept
-0.486
0.781 0.39
SAS outputses[1] -0.482 0.243 3.94 0.382 0.143 0.971 ses[2] 0.241 0.193 1.55 1.619 0.761 3.474 read 0.004 0.015 0.09 1.232 0.308 4.939 Intercept
-0.486
0.781 0.39
My SAS statement is as follows:ses 1 -0.482 0.243 3.94 0.486 0.211 1.118 ses 2 0.241 0.193 1.55 1.000 0.513 1.952 read 0.004 0.015 0.09 1.004 0.976 1.034 proc logistic data= "">
class ses PARAM = REF;
The "Whole Model Test" generated by JMP is the same as the Likelihood ratio Chi-Square in SAS for "Test Global Null Hypothesis."model female = ses read / CLODDS = BOTH; run; Can someone explain the reasons for these differences in both the categorical variable with >2 response levels (such as 'ses' in this example) and why the continuous variable 'read' is different? Since the estimates are the same it must be a difference in how the two packages calculate the odds ratios. -- Sam Brar, MD Kaiser Permanente/UCLA Fellow, Division of Cardiology Office: 323-783-4915 Fax: 323-783-5509 |