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

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 ...

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 ...

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 ...

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 ...

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, ...

Generalized Linear Models Generalized Linear Models Course Topics Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, ...

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.