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 …

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 …

Oct 16, 2014 · In this case, either logit or LogisticRegression could be used to fit the logistic regression model with the two indicator variables. The choice between the two methods may …

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 + …

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 …

Jun 8, 2020 · This is a fixed effects logistic regression. A mixed-effects logistic regression would be when we have $$ g(\theta) = X\beta + Z\gamma $$ with $\gamma\sim\mathcal …

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 …

Nov 11, 2023 · In general with regression, and particularly with binary (e.g., logistic) regression, it's a mistake to do separate models for each predictor. Even in ordinary linear regression, a …

The logistic regression told us that the coefficients for product 1 and product 2 were 0.08398 and 0.04474. In the lmer model with interaction, the effects were 0.08399 for time (which is for …