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]

I've been following this discussion, and I admit the information you've provided is too vague to provide any specific advice. It is very hard to give advice without appropriate context and unfortunately, as is often the case on a forum like this, it can be difficult to provide the appropriate context. Here are my thoughts which may not be helpful at all:

I'm not really sure what you mean by a "multidimensional response"?  You later indicate these are "multivariate responses" which simply means you have multiple response variables.  You suggest they are not independent, how did you determine this? It would be nice to know how the 5 nominal responses correlate, but that may not be possible to assess.  I'm wondering if you can create a response variable that is a function of the 5 nominal responses?  Perhaps an ordinal scale instead of a nominal response?

[#3]

As stated in the OP, each observation has 3 continuous predictors and 5 indicator responses. The state of Y1 (1 or 0) depends, in part, on the states of Y2-5. That's because humans are doing the indicating of each of the Y1-5 in each case. Their tendency to indicate Y2 = true, for example, will be influenced, in part, by their indication of Y1 and the other Ys. 

It seems to me that if I model Y1 ~ X1 + X2 + X3 then I am ignoring information coming from the states of Y2-5. The response variable is a vector. I want to know how each predictor affects that vector. 

[#4 · Accepted solution]

You can ignore me, but I'm even more confused.  I don't understand "states of Y2-Y5"?  Are these different characteristics of the "sample"? Are humans "observing" a sample and determining 0 or 1?  What does 0 and 1 mean?  Arte these operationally defined (per Deming)?

If the response is a vector, why don't you quantify magnitude and direction?

If the response is a categorical response from human sensory perception, I would try to create more categories and use ordinal scales.  If you are concerned with human bias, make sure the scale is a comparison scale not based on emotion.  I would add repeated measures both within human and between human (same "sample" measured at least twice by same human and them other humans).  You can use simple plots to see if there consistent biases from a particular human(s) and you can use averaging to reduce each of those components of variation.

[#5]

It's a vector in the mathematical sense -- that there is a list of values rather than a single element. 

I'm not sure how else to describe a set of 5 indicator values.

In each observation, an assessor is indicating for each of 5 questions, whether it was true or false (on or off, 1 or 0). It's a basic binary indicator. What is not understandable about a binary indicator response???

The values are judgements by people who are assessing each of 5 possible 'reasons' in each observation. Each of the five responses can be yes or no, without exclusion of the others being yes or no. So, theoretically, the response vector could take on 32 different combinations.