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 ...

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 ...

Example applied: Cooking time in fixed and segregating common bean populations. ... (based on generalized linear models) in several high impact journals and concluded that the use of the latter method ...

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 ...