# What's the difference between binomial regression and logistic regression?

I've always thought of logistic regression as simply a special case of binomial regression where the link function is the logistic function (instead of, say, a probit function).

From reading the answers on another question I had, though, it sounds like I might be confused, and there is a difference between logistic regression and binomial regression with a logistic link.

What's the difference?

Logistic regression is a binomial regression with the "logistic" link function:

$$g(p)=\log\left(\frac{p}{1-p}\right)=X\beta$$

Although I also think logistic regression is usually applied to binomial proportions rather than binomial counts.

• What do you mean by logistic regression being usually applied to proportions rather than counts? Suppose I'm trying to predict whether people will attend a party or not, and that for a particular party, I know that 9 people attended and 1 did not -- do you mean that logistic regression takes this as one training example (i.e., this party had a success rate of 0.9), while binomial regression with a link would take this as 10 training examples (9 successes, 1 failure)? – raegtin Jul 18 '11 at 1:35
• @raehtin - in both cases it would be $1$ sample/training case, with $(n_i,f_i)=(10,0.9)$ and $(n_i,x_i)=(10,9)$ respectively. The difference is the form of the mean and variance functions. For binomial, mean is $\mu_i=n_ip_i$, the canoncial link is now $\log\left(\frac{\mu_i}{n_i-\mu_i}\right)$ (also called the "natural parameter"), and the variance function is $V(\mu_i)=\frac{\mu_i(n_i-\mu_i)}{n_i}$ with dispersion parameter $\phi_i=1$. For logistic we have mean $\mu_i=p_i$, the above link, variance function of $V(\mu_i)=\mu_i(1-\mu_i)$ and dispersion equal to $\phi_i=\frac{1}{n_i}$. – probabilityislogic Jul 18 '11 at 15:18
• With logistic, the $n_i$ is separated out from the mean and variance functions, so can be more easily taken into account via weighting – probabilityislogic Jul 18 '11 at 15:20
• Ah, got it, I think I see. Does this mean that they produce equivalent results (simply arrived at from a different way)? – raegtin Jul 19 '11 at 19:39
• @raegtin - I think so. The GLM weights, $w_{i}^{2}=\frac{1}{\phi_i V(\mu_i)[g'(\mu_i)]^{2}}$, are equal in both cases, and the link function produces the same logit value. So as long as the X variables are also the same, then it should give the same results. – probabilityislogic Jul 20 '11 at 12:38

Binomial regression is any type of GLM using a binomial mean-variance relationship where the variance is given by $\mbox{var}(Y) = \hat{Y}(1-\hat{Y})$. In logistic regression the $\hat{Y} = \mbox{logit}^{-1}(\mathbf{X}\hat{\beta})=1/(1-\exp{(\mathbf{X}\hat{\beta})})$ with the logit function said to be a "link" function. However a general class of binomial regression models can be defined with any type of link function, even functions outputting a range outside of $[0,1]$. For instance, probit regression takes a link of the inverse normal CDF, relative risk regression takes as a link the log function, and additive risk models take the identity link model.