1. ## question about Logistic regression

Hi, I've wondered about logistic analysis.

In glm(generalized linear model), link function value of mean = linear predictot. So, classical model's responses is described as g(E(Y))=Xb where X:design matrix, b:coefficient vactor, and g is link function since Y~normal distribution.

But, in the case of logistic model, responses are binomial distribution(n_i,p_i).
So I think that g(n_i*p_i )=Xb where g is a link function.
However, My text book says g(p_i)=Xb, i.e., the parameter n_i is not considered.

Why is this occured?

2. ## Re: question about Logistic regression

I guess you are working with Bernoulli distribution (with Binary outcomes only) so you do not have inside.

But if you really have some count data, possibly > 1, with both are unknown, then it maybe another story. I am not sure.

3. ## Re: question about Logistic regression

Whether you're modeling binomial or bernoulli the idea is still the same. We consider the n_is to be fixed quantities so it doesn't matter if we model n_i*p_i or p_i. But it makes more sense to just model the p_i since for each observation n_i could be different. If you wanted to when you have binomial data you could always pretend like you have bernoulli data just by creating a dataset of success/failure from each binomial trial.

#### Posting Permissions

• You may not post new threads
• You may not post replies
• You may not post attachments
• You may not edit your posts