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Re: Logistic regression with multiple outcome variables - Odds ratios in JMP

Yes, each row (individual) will get a result from the formula for the odds.

Yes, you can use the mean odds. Use Table > Summary or Analyze > Tabulate to get the results for each group or subgroup.

The hand calculation is as you say Pr(group A) divided by Pr(not group A) or whatever. The odds ratio would be the ratio of the odds under different conditions (treated, untreated).

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Re: Logistic regression with multiple outcome variables

I am using JMP 13 Pro, not a student version.

Re: Logistic regression with multiple outcome variables

Are there tutorials/videos for logistic regression with multiple outcomes?

Re: Logistic regression with multiple outcome variables

I checked our learning assets but found no tutorials about nominal logistic regression. (See items from selecting Learning JMP on the JMP home page menu.) I then checked our YouTube account and found this tutorial about Multiple Logistic Regression. There is also Logistic Regression Introduction with Tutorial in JMP on YouTube. It covers logistic regression more thoroughly but only for the outcome. It does not cover multi-nomial logistic regression.

We offer this training course, which covers this topic: Analyzing Discrete Responses. We cover the origin, use, and interpretation of such models as well as how to preform this regression in JMP.

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Re: Logistic regression with multiple outcome variables

You can't do logistic regression with multiple dependent variables in one run of logistic. But perhaps you have ONE dependent variable - behavior of gibbon - with multiple levels?  How is "behavior" operationalized? Is the data something like this:

Case ID Num People      Behavior

1               3                    A

2               2                    B

3              5                     A

4              2                    C

etc.? Or does each gibbon engage in multiple behaviors? Or is each gibbon engaged in multiple cases? (in that case, you'd need some form of multi-level model, probably with GLIMMIX?

Re: Logistic regression with multiple outcome variables

It is like you have laid out.  The gibbon only does one behavior at a time, but it has several behaviors it can do, such as feed, travel, vocalize, groom, etc.  What I am trying to do is see how the number of people present affects the likelihood of the gibbon doing a certain behavior e.g. does the gibbon reduce time spent feeding when more people are present.  The logistic regression tells gives me a p value for the entire model, so I can see that number of people does affect gibbon behavior, but what I would like to do is see which individual behaviors are driving the model - I'd like some sort of stats with p values that show me which behaviors are actually changing.  All I am doing now is looking at the output figure and describing how the behaviors change.  Here is what the output looks like.  I tried to insert the figure, but it wasn't working.  I have figured out though, that I cannot do the odds ratio test because my response (behavior) has more than 2 variables.

Logistic Fit of Behavior By total humans

Whole Model Test

 Model -LogLikelihood DF ChiSquare Prob>ChiSq Difference 37.2401 11 74.48025 <.0001* Full 1162.7796 Reduced 1200.0197

 RSquare (U) 0.0310 AICc 2371.15 BIC 2468.39 Observations (or Sum Wgts) 660

 Measure Training Definition Entropy RSquare 0.0310 1-Loglike(model)/Loglike(0) Generalized R-Square 0.1096 (1-(L(0)/L(model))^(2/n))/(1-L(0)^(2/n)) Mean -Log p 1.7618 ∑ -Log(ρ)/n RMSE 0.7987 √ ∑(y-ρ)²/n Mean Abs Dev 0.7918 ∑ |y-ρ|/n Misclassification Rate 0.6727 ∑ (ρ≠ρMax)/n N 660 n

Parameter Estimates

 Term Estimate Std Error ChiSquare Prob>ChiSq Intercept[Drink] Unstable 9.3119005 1537.0201 0.00 0.9952 total humans[Drink] Unstable -13.895268 1537.0192 0.00 0.9928 Intercept[Feed] -1.5746952 0.2067421 58.01 <.0001* total humans[Feed] 0.45943506 0.0703651 42.63 <.0001* Intercept[Groom] -3.8964691 0.7363762 28.00 <.0001* total humans[Groom] 0.17142825 0.2657919 0.42 0.5189 Intercept[Groom Recipient] -5.9062151 1.021258 33.45 <.0001* total humans[Groom Recipient] 0.60419887 0.1604264 14.18 0.0002* Intercept[Hang] -3.4626526 0.5849713 35.04 <.0001* total humans[Hang] 0.187694 0.2073532 0.82 0.3654 Intercept[Not Visible] -0.9158515 0.1862225 24.19 <.0001* total humans[Not Visible] 0.35203714 0.0696868 25.52 <.0001* Intercept[Other] -7.0115111 1.4481919 23.44 <.0001* total humans[Other] 0.71085044 0.1806613 15.48 <.0001* Intercept[Rest - Sleep] -2.0491431 0.2866187 51.11 <.0001* total humans[Rest - Sleep] 0.25952368 0.0987496 6.91 0.0086* Intercept[Rest - Still] -1.703556 0.2215666 59.12 <.0001* total humans[Rest - Still] 0.40532436 0.0739462 30.05 <.0001* Intercept[Self groom] -5.34457 1.6699724 10.24 0.0014* total humans[Self groom] 0.09443149 0.6681153 0.02 0.8876 Intercept[Travel] -1.709588 0.2321228 54.24 <.0001* total humans[Travel] 0.34947804 0.0784157 19.86 <.0001*

For log odds of Drink/Vocalize, Feed/Vocalize, Groom/Vocalize, Groom Recipient/Vocalize, Hang/Vocalize, Not Visible/Vocalize, Other/Vocalize, Rest - Sleep/Vocalize, Rest - Still/Vocalize, Self groom/Vocalize, Travel/Vocalize

Re: Logistic regression with multiple outcome variables

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.

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Re: Logistic regression with multiple outcome variables

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

Re: Logistic regression with multiple outcome variables

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.

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Re: Logistic regression with multiple outcome variables

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: