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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 - Odds ratios in JMP

Hi Mark,

I am performing a multinomial logistic regression for an outcome variable with three levels.  I have been trying to figure out how to find the odds ratios and finally came across this previous answer from you.

How do I save the probability formulas?

Thank you in advance.

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

Click the red triangle at the top of the platform and select Save > Save Probability Formula.

Learn it once, use it forever!

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

Thank you for the prompt response.

Alternative to saving the probability formulas and calculating the odds ratios myself, I had independently saw at the bottom of the parameter estimates data display on my multinomial logistic model that is says "for log odds of Outcome A/Outcome C, Outcome B/Outcome C" where Outcome C is my reference outcome.  Is it possible that I can just calculate the odds ratio by transforming this data into a data table, inserting a new column with the formula exp(Estimate)?

I found a separate post that references this as well -->  https://community.jmp.com/t5/Discussions/Logistic-regression-with-multiple-outcome-variables/m-p/110...

It seems almost too easy to be true.

Thank you again.

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

It is not true. That way is also not easy at all. It is more difficult than using the Save command. The parameter estimates are not estimates of the odds or odds ratios. That note is just a re-statement of the logistic regression problem. You have the linear predictor on the right side of the model equation and the logit function on the left side. The note that you see at the bottom of the Parameter Estimates table is just a record of what logits were used with the linear predictors.

If you save this table of estimates, then you have to create a column formula to compute the logit value for each row. Then you have to add another column formula for each response level to back-predict the probabilities. That is what the Save > Save Probabilities command does for you.

Learn it once, use it forever!

Re: Logistic regression with multiple outcome variables

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

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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.

Learn it once, use it forever!

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