Generalized linear mixed model for segregation distortion analysis

Bayesian Inference for Spatial Beta Generalized Linear Mixed Models

In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...

Multilevel mixed linear model analysis using iterative generalized least squares

In the social and other sciences, data are often structured hierarchically. Thus, for example, workers are grouped into workplaces, individuals into households, animals into litters, and subjects can be studied repeatedly, so giving rise to measurements grouped within individuals. It has long been recognized that the existence of such 'clustering' presents particular problems of model specifica...

Generalized Linear Mixed Models

Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts). McCullagh and Nelder [32] describe these in great detail and indicate that the term ‘generalized linear model’ is due to Nelder and Wedderburn [35] who described how a collection of seemingly disparate statistical techniques coul...

Generalized Linear Mixed Models

Practical likelihood analysis for Spatial Generalized Linear Mixed Models

We propose a standard approach to make inference for spatial generalized linear mixed models using Laplace approximation. Based on analysis of two datasets previous analysed in literature, we compare our approach with different approaches. The first the rhizoctonia root rot dataset is an example of Binomial SGLMM and the second rongelap dataset is an example of Poisson or Negative Binomial SGLM...