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Generalized Linear Mixed Models with Applications in Agriculture and Biology (1st ed. 2023)
Loot Price: R1,548
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Generalized Linear Mixed Models with Applications in Agriculture and Biology (1st ed. 2023)
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This open access book offers an introduction to mixed
generalized linear models with applications to the biological
sciences, basically approached from an applications perspective,
without neglecting the rigor of the theory. For this reason, the
theory that supports each of the studied methods is addressed and
later - through examples - its application is illustrated. In
addition, some of the assumptions and shortcomings of linear
statistical models in general are also discussed. An alternative to
analyse non-normal distributed response variables is the use of
generalized linear models (GLM) to describe the response data with
an exponential family distribution that perfectly fits the real
response. Extending this idea to models with random effects allows
the use of Generalized Linear Mixed Models (GLMMs). The use of
these complex models was not computationally feasible until the
recent past, when computational advances and improvements to
statistical analysis programs allowed users to easily, quickly, and
accurately apply GLMM to data sets. GLMMs have attracted
considerable attention in recent years. The word "Generalized"
refers to non-normal distributions for the response variable and
the word "Mixed" refers to random effects, in addition to the fixed
effects typical of analysis of variance (or regression). With the
development of modern statistical packages such as Statistical
Analysis System (SAS), R, ASReml, among others, a wide variety of
statistical analyzes are available to a wider audience. However, to
be able to handle and master more sophisticated models requires
proper training and great responsibility on the part of the
practitioner to understand how these advanced tools work. GMLM is
an analysis methodology used in agriculture and biology that can
accommodate complex correlation structures and types of response
variables.Â
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