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etiennel · ‎01-17-2019

I'm using a constraint like this number TotalValues = 1000; con sum{i in setgroup}(NewValue[i] * IsItTrue[i]) >= TotalValues; I want to use the result of the "if" as a constraint, that for each value in setgroup, IsItTrue[i] is either 0 or NewValue[i] is >= 20; The "if" would give a value, but how do I specify the "g" in your example or that the "something" is the pass/fail value of the constraint? If I write con and{i in setgroup} (IsItTrue[i] = 0 or NewValue[i] >= 20); it gives me a synthax error and I don't know why.

etiennel · ‎01-17-2019

Greetings, I'm using Proc Optmodel. A part of my code is: set setgroup = 1..10; number IsItTrue {setgroup}; var NewValue {setgroup}; Since "IsItTrue" is group of boolean values (0, 1), I want to add a constraint that if IsItTrue = 0, the constraint is acheived, but if IsItTrue = 1, the constraint is acheived if NewValue is above or equal 20. So one constraint IsItTrue (or IsItTrue * NewValue) (<) = 0 OR another constraint IsItTrue * NewValue >= 20. How can I write this in Optmodel? At another place in the model, I use NewValue * IsItTrue to keep only the NewValue of IsItTrue and make sure the total is higher than 3000, but this new constraint is on individual values, not the total, and it is for different sides (one is lower or equal than 0 while the other is above or equel 20). Thank you.

etiennel · ‎10-06-2016

I have solved the problem by recreating the linear equation and getting the probabilities "by hand". It gives me the same results exactly for those that had results, and it gives me results for all the others as well. Still, it seems like a weird bug.

etiennel · ‎10-04-2016

Greetings, I'm running a Proc logistic on some data. When I score the training data, less than 1% of predicted values have P_0 = P_1 = 0, but I'm score my testing data, more than 60% of the data have P_0 = P_1 = 0, ergo no prediction. Since it's a linear equation, shouldn't P_1 always have a value and shouldn't P_0 simply equal 1-P_1? Am I missing something? How can I find what is missing from the data to get a prediction on the testing data? Thank you for your help.

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