![]() ![]() If I collapse them into one proportion (What proportion of the friends do action X?) the results are pretty clean, but I lose information about the These are universal function approximators: You could train a neural network with one hidden layer. Or, what if I told you that you can do this automagically? If you tried a linear combination of features, you could also try a quadratic other combinations of features (e.g friends). I forgive myself, now I can study: How self-forgiveness for procrastinating can reduce future procrastination. Looks like you're having a hard time finding the right representation. If I collapse them into one proportion (What proportion of the friends do action X?) the results are pretty clean, but I lose information about the ranking.ĭoes anybody have a suggestion of a way of coding that will preserve more information? I am hoping for something that will preserve the ranking of the friends. second friend is highly significant but not first.) I think this is probably because there is high correlation between them? Further, there is a fair number of observations that don't list 3 friends (~25%) which can't be used in such an analysis. If I treat the predictors as separate variables, I get strange results (i.e. ![]() I regress forgive me code#I was hoping to fit a logistic regression model, but I am having trouble coming up with a way to code the predictors, however. The outcome is Person A performing the action (Yes/No) I have a dataset where I am attempting to answer the question: How much do a Person A's friends behaviors influence Person A's willingness to do action X? The data from the friends (predictors) looks like: Walking in the light is demonstrated not by the denial of sin but by confessing it and abandoning it. 9 John now confronts us with our second definite test of obedience. Forgive me if this is has been asked before, but I couldn't find anything (maybe I was using the wrong keywords) 9 If we confess our sins, he is faithful and just and will forgive us our sins and purify us from all unrighteousness. I looked around but did not find a discussion re: techniques for using the MOEs to adjust the estimates / or as a. ![]() I am fairly new to regression modeling and was hoping to get some help. Im new here, forgive me if this is off-topic. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |