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
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