This is a group of theoretical questions based on respective scenarios pertaining to the meaning of the intercept in the logistic procedure.   Scenario 1: I am working with a binary response model. There is only one categorical predictor.  The predictor has levels 1 and 2, and the param method is reference cell coding. The intercept (0.4961) is a logit probability of the event at the reference level (level 2). The estimate (-0.2626) is difference between logits of level1 and the reference level (level2).  Now I re-run the code with the noint option that supresses the intercept. Did I just make the probability of event at the reference level zero? The estimate becomes 0.2335 = 0.4961 - 0.2626, suggesting a positive answer, but perhaps there is a way to retrieve the 0.4961 number from the output. Question 1: Is there a way to retrieve that 0.4961 number (logit probability of the event at the reference level) from the output generated with the noint option?   Scenario 2: I added a second, continuous predictor to the previous model. Without the noint option, how does that affect the meaning of the intercept? Is the intercept now a sum of contribution to the intercept from adding the continuous predictor and the logit probability of the event at the reference level?   Scenario 3:  I ran the model from the second scenario with the noint option.  What is the meaning of estimate for level1 category now? Is it still the difference between its logit and the logit of the reference level? Is there a way to retrieve the logit of the reference level from the output?    Any thoughts are greatly appreciated. ... View more

I am doing reference cell coding with a binary categorical predictor. According to max likelihood table, Intercept = 0.2335, and that is the predicted logit probability of the reference level. However, the effect plot shows a dot at >0.50 for the predicted probability of that level. Why is that? Please let me know possible explanations. proc logistic data=have plots(only)=(effect oddsratio); class x(ref='<=60 min')/ param=ref; model y(event='1')= x / clodds=pl; run;   ... View more

I provided the data from the following function to Proc Logistic: P = 1 / [ 1 + e^(- 0.005 * x) ] Sigmoid probability distribution has the following form: P(event) = 1 / [ 1 + e^( - (a + bx) ], where x is the predictor variable. Therefore, a = 0 and b = 0.005. Then, I simulated binary response values as follows: if P >= 0.6 then y = 1; else y = 0. I used Proc Logistic to model y( event = '1') = x. The Logistic procedure confidently returned the following parameters: a = -182.5 and b = 2.2401. Why are they different from what I provided?   ... View more