You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Introduction to Generalized Linear Models In this two day course, we provide a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be continuous or discrete ...

Duration: 12h. In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial ...

Explanatory IRT models based on generalized linear mixed models (GLMM) are very flexible in modeling multiple item context effects. The aims of this article are 2-fold. First, we show how different ...

In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

Introduction to Generalized Linear Models In this two day course, we provide a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are ...