Logistic regression with multiple outcome variables

[#1]

I am running logistic regression analyses on a dataset where the outcome has multiple categorical variables.  In this case, I am using total number of humans present (continuous variable) to predict the behavior of gibbons (the behaviors are categorical).  I am trying to use the parameter estimates to determine which specific behaviors are affected by the number of humans, but I am having trouble interoperating the results.  I can see the results for each individual behavior, and the Wald test to show whether that behavior is affected by number of people, but the last behavior alphabetically on my list (vocalize) does not output any numbers, so I do not know how to see if this variable is affected.  Am I interoperating these results correctly?  How do I see if the last behavior (vocalize) is affected by number of people?

[#2]

The Nominal Logistic Regression personality in Fit Model should help. You can get the odds ratios by clicking on the red triangle at the top of the platform. Also, you can use the prediction profiler to estimate the probability of all the levels for any condition, and then compute the odds. Now change the condition (predictor level) and repeat the calculation. Now compute the odds ratio.

[#3]

Can you give me more information on the predictor profiler?  I am able to get that figure when I run the test with fit model instead of fit y by x, but I am not sure how to estimate the odds for each prediction.  What I got was a figure like the one I made, but with red horizontal lines on it as well.  There are several options in the red drop down arrow, but I am not sure which one tests each prediction.  And indeed, drink was a rare behavior, I assumed that is why it was unstable. 

I am having a problem running the odds ratio in that jmp won't give me that option because there are more than 2 behaviors.  I think that is what I need to do, but I can't seem to make jmp do it.  The option does not appear on the drop down triangle unless I make up a new data sheet that only has 2 possible outcomes in the response variable.  I can use the predictor profiler, but I am not really familiar with how it works.

Thanks again

[#4]

Use the LRTs to judge signficance and choose your model. Once that part is done, then use the model to compute the odds ratios.

Use the prediction profiler to compute the probabilities and then the odds of an event versus the non-event for a given predictor level. Change the predictor level, get the updated probabilities, and compute the new odds. You can then compute the ratio. This way you can compute the odds ratio for anything.

[#5]

The fact that two parameter estimates are unstable indicates that DRINK was probably an uncommon behavior.

The easiest way to interpret logistic regression is via odds ratios - which are exp(parameter estimate) and predicted probabilities. I don't know JMP, but if you have SAS/STAT I wrote a paper on multinomial logistic.  It's here:

[#6]

As an update, this is what I am doing now:  I am running each behavior individually using a standard binomial logistic regression, scoring behaviors as 1 for when they occurred and 0 for all other behaviors (only when the nominal logistic regression was significant).  I am only doing this for common behaviors.  This ends up being what I wanted to compare (one behavior vs. all other over number of people).  The only issue I am having now is whether or not I need to do a correction for running multiple tests (such as bonferroni).  I am running only 5 behaviors (feed, not visible, sleep, rest, travel, vocalize), so I am not sure if the correction is needed.  I know bonferroni can be a little conservative, so I am a little hesitant to use it unless it seems really necessary.

[#7]

A bit of a simplistic GUI based solution: in the parameter estimates table I double clicked the table. I chose the "Make into Data Table" option. Once JMP produced the data table I created a new column which I named odds ratio. Then I chose the formula option in the Cols menu. Once inside the formula window I wrote the formula: exp(Estimate). The output was the odds ratio for the estimates. To double check, I copy pasted the estimates into R and used the exp function in R. It gave the same results. HTH