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 ...

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 ...

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 ...

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 ...

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, ...

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 ...

Sound Bites • The development of generalised linear models (GLMs) led to other important advances in statistics, particularly when the assumption of independence between responses is violated.