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

T. Timothy Chen, Log-Linear Models for Categorical Data With Misclassification and Double Sampling, Journal of the American Statistical Association, Vol. 74, No. 366 (Jun., 1979), pp. 481-488 ...

Log-Linear Model Analysis When the response functions are the default generalized logits, then inclusion of the keyword _RESPONSE_ in every effect in the right-hand side of the MODEL statement induces ...

We propose an extension of graphical log-linear models to allow for symmetry constraints on some interaction parameters that represent homologous factors. The conditional independence structure of ...

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 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 ...

Topics include: general theory of regression and generalised linear models, linear regression, logistic regression for binary data, models for ordered and unordered (nominal) responses, log-linear ...

Topics include: general theory of regression and generalised linear models, linear regression, logistic regression for binary data, models for ordered and unordered (nominal) responses, log-linear ...

Example 22.4: Log-Linear Model, Three Dependent Variables This analysis reproduces the predicted cell frequencies for Bartlett's data using a log-linear model of no three-variable interaction (Bishop, ...

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, ...