There are many ways to choose what variables go in a regression model, some decent, some bad, and some terrible. One may simply browse the publications of Sander Greenland, many …

Dec 27, 2014 · Logistic regression is a binary classifier (edit: can be used as a binary classifier), and therefore you can use the standard metrics for classifiers. The metrics you use are the …

To understand why "[t]he response is either 0 or 1 [but] the predictions are probabilities between 0-1", you need to understand the type of model you are working with. Strip away the …

$\begingroup$ @JohnSteedman: I don't understand the distinction you're drawing between the "stuff we can't see" in linear regression & the "unseen variation" in logistic regression. In either …

Jun 14, 2015 · In general, the log odds ratios of a logistic regression model tend toward a biased factor of $2\beta$ because of non-collapsibility of the odds ratio and zero cell counts. In …

Jan 1, 2021 · Instead of using robust SE, a researcher should perform specification tests after fitting a logistic regression model to screen lack of fit and examine potential nonlinear functions …

The following code can be used, for example, to generate three independent standard normally distributed variables ("x1", "x2" and "x3") and one binary variable "y", where "y" is modeled as …

Nov 6, 2018 · For regression in general, including logistic regression, including dummy variables as independent variables entails having a reference group. That is, you you have dummies for …

Mar 3, 2014 · The logistic regression model is of the form $$ \mathrm{logit}(p_i) = \mathrm{ln}\left(\frac{p_i}{1-p_i}\right) = \beta_0 + \beta_1 x_{1,i} + \beta_2 x_{2,i} + \cdots + …

Mar 27, 2015 · Because the logistic link is the canonical link for a binomial family, using logistic regression for g-computation guarantees consistency when the treatment is randomized, even …