Multiple indicator responses and multiple continuous predictors

[#1]

Hi again, I'm using JMP Pro 15.2.1. I have a dataset where each observation has three continuous predictors and five indicator (y/n) responses. For each observation, any or all of the indicators may be present. I want to model the simultaneous effects of several predictors on a multidimensional response variable. 

I can't seem to find the correct approach to this data. JMP balks at multiple categorical responses or the fact that my predictors are continuous.  

[#2]

Hi @gchesterton ,

I might be misunderstanding what you are trying to do but this should not be a problem in JMP.

Analyze > Fit Model.

Specify your 5 binary responses in the Y role.

Add your 3 continuous predictors to the Construct Model Effects outline.

Run.

Is that what you have tried? If so, what was the problem?

It always helps if you can attach an illustrative example .jmp data table. Please no sensitive data though.

I hope this helps,

Phil

[#3]

Thanks @Phil_Kay , your proposed approach is precisely what I did and what I expected to work. But JMP is telling me that I can include only one nominal response. As you might guess, my nominal responses are each in their own columns. Obviously I could stack them into a single data column and add a source label column.  

[#4]

I'm not able to replicate that behaviour.

Again, attaching example data to illustrate the query would really help.

Phil

[#5]

I've attached a comma separated text file. This is actual raw data with the headers changed. The predictors should be standardized/transformed but I don't think that's the issue at the moment.

[#6]

@Phil_Kay in addition to that dataset, here's my dialog in JMP. 

[#7]

I opened the CSV file in the current release, JMP Pro 17.2, and changed the modeling type of all Y columns to Nominal. I completed the Fit Model launch dialog as you indicated. I received the following results without any errors:

(Note that the report for the first four Ys is closed for sake of space.)

[#8]

Hi, @gchesterton .

I still can't reproduce your problem. Maybe because I am on JMP Pro 17.1.0 on Windows?

I suggest you contact tech support (support@jmp.com).

In any case, it is probably good to understand that what you will get from Fit Model will, in fact, be separate models for each response, but all in one JMP report window. So you could work around your issue by fitting each model separately - the models will be the same.

[#9]

Indeed I can produce a separate model for each nominal response. But my motivation for inserting all five response variables in the response window was not to simplify the process of doing five separate models. Instead, my thought is that I'm dealing with a multi-dimensional response. These responses are not independent, so I thought by including multiple response variables in a single model fit that it would recognize them as a multivariate response. In other words, the joint state of Y1-5 with predictors X1,2,3.

I could model Y1 ~ X1+X2+X3 + Y2+Y3+Y4+Y5 but I think what I really want is  Y1 + ... + Y5 ~ X1 + X2 + X3. 

[#10 · Accepted solution]

Makes sense, @gchesterton . You want to simultaneously understand the effects of X1 ... X3 on all responses Y1 ... Y5.

We could get very philosophical about what you mean by "responses are not independent" and we could explore sophisticated modelling techniques that treat Y1 ... Y5 as some kind of grouped, multivariate response.

But the pragmatic approach that people will use 99% of the time is to build separate models for each of Y1 ... Y5 against X1 .... X3 and then profile them together:

These are still separate models.

The process to do this is to save the Probability Formula for each model:

And then add the Prob[1] prediction formula column to Y, Prediction Formula role in Graph > Profiler.

I hope this helps,

Phil