Obtaining 95% CI from predicted P (logistic)

[#1]

Hello,

I'm doing logistic regression in JMP 16. I would like to obtain the 95% CI around predicted P for some independent variables using the P as a measure of strength of association. I know the math to calculate the 95% CI from linear predictors but I need help pulling out the necessary information once I run the model. I can obtain the linear predictor by saving to a new column, but I also need the standard error of the linear predictor. How do I obtain that? I have done this in R but I would prefer to learn in JMP.

Thanks! 

[#2 · Accepted solution]

do the logistics modeling use Fit model you can turn on CI in the hotspot menu:

This will give you CI on the parameter estimates table.

From the same hotspot you can also save the probability formula's.

[#3]

Thanks for reply. Turning on CI gives me the CI around the chiSq tests for each term, but what I'm after is obtaining a CI around the expected value (probability of "1" or "yes"). To calculate the CI, one needs the linear predictor as well as the SE.  I can get the linear predictor and the expected value from the save drop down choices but I can't find where to obtain the SE of the linear predictor.

95% confidence interval formula:

Lp-1.96* se(Lp), Lp+1.96*se(Lp)

Lp - linear predictor

[#4]

...and to finish the thought, this is the formula to get CI around probabilities (expected values) once you have the linear predictor interval:

Lp interval [a, b]

Probability interval:

[exp(a)/1+exp(a), exp(b)/1+exp(b)]

[lower, upper]

[#5]

Did you notice the Std Error column in the parameter estimates table?

[#6]

Thanks much! My bad. I thought it was for something else.

[#7]

Cool! If you are satisfied, please mark the topic as solved so that other people know it is done :)