Using log-linear models, we propose the following procedure (Fig. 1) for inferences regarding the main genetic effect and its interactions.

In this chapter, we propose a log-linear model for the biases observed when analyzing model communities data. Our model expands the recent work from McLaren, Willis and Callahan (MWC) [eLife, 8:e46923 ...

A log-linear model for predicting magazine exposure distributions is developed and its parameters are estimated by the maximum likelihood technique. The log-linear model is compared empirically with ...

Robin J. Evans, Thomas S. Richardson, Marginal log-linear parameters for graphical Markov models, Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 75, No. 4 ...

Linear models have the disadvantage that allelic effect estimates cannot be interpreted, directly, in terms of the odds ratio (OR), although approximations on the log-odds scale can be obtained ...

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 regression model, ...