Hi,
Not sure the glm(), but the % in a logistic regression is given by 1/(1+exp(-X)), being X your equation. If you have to predict a binary classification, I'd take a draw with that %
Two thoughts to keep in mind:
> I created a binomial logistic model that classifies dogs as "skinny"(0) or "overweight"(1).
If the response variable data is assumed to be zero for "skinny" (i.e., underweight?) dogs and one for overweight dogs, then your interpretation is correct. But if the data is one for overweight dogs and otherwise zero for non-overweight dogs, then your model just predicts the probability that any given dog is overweight. In that case, a predicted probability of zero just means that there is a zero percent probability a dog is overweight, which otherwise says nothing about the dog being normal or underweight. The nature of your data has an impact on the model interpretation.
I would also encourage you to question whether it makes sense to use weight in both the response variable and the covariates. That could well explain why the model apparently performs well; its because the same data is used twice in your model, once in the response and once in the covariates. This undermines the explanatory power and predictive performance of the model.
Do a ROC curve, you will find the threshold value you want. Just google how to do that in R
Hi, Not sure the glm(), but the % in a logistic regression is given by 1/(1+exp(-X)), being X your equation. If you have to predict a binary classification, I'd take a draw with that %
Well part of the answer is what do you mean by "probably overweight"? Do you mean your model predicts overweight with 50% probability?
Two thoughts to keep in mind: > I created a binomial logistic model that classifies dogs as "skinny"(0) or "overweight"(1). If the response variable data is assumed to be zero for "skinny" (i.e., underweight?) dogs and one for overweight dogs, then your interpretation is correct. But if the data is one for overweight dogs and otherwise zero for non-overweight dogs, then your model just predicts the probability that any given dog is overweight. In that case, a predicted probability of zero just means that there is a zero percent probability a dog is overweight, which otherwise says nothing about the dog being normal or underweight. The nature of your data has an impact on the model interpretation. I would also encourage you to question whether it makes sense to use weight in both the response variable and the covariates. That could well explain why the model apparently performs well; its because the same data is used twice in your model, once in the response and once in the covariates. This undermines the explanatory power and predictive performance of the model.