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

Lance A. Waller, Daniel Zelterman, Log-Linear Modeling with the Negative Multinomial Distribution, Biometrics, Vol. 53, No. 3 (Sep., 1997), pp. 971-982 ...

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 ...

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 ...

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 ...

Linear Models Contrasted with Log-Linear Models Linear model methods (as typified by the Grizzle, Starmer, Koch approach) make a very clear distinction between independent and dependent variables. The ...

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.

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, ...