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2018•arXiv (Cornell University)Open access

Analysis of a longitudinal multilevel experiment using GAMLSSs

Gustavo Thomas, Alexandre Igor de Azevedo Pereira, Cristian Marcelo Villegas Lobos, Clarice Garcia Borges Demétrio

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Abstract

The standard procedures for analysing hierarquical or grouped data are by (non)linear mixed models or generalized mixed models. However, the generalized additive models for location, scale and shape (GAMLSSs) also allow different types of random effects to be included in the model formulation. Even though already popular in many areas of research, this type of models have not been found to be used for mixed modeling purposes yet. Therefore, this paper describes the analysis of an experiment with plants' growth using mixed GAMLSSs, comparing it to a linear mixed model approach.

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What this paper is about

The standard procedures for analysing hierarquical or grouped data are by (non)linear mixed models or generalized mixed models. However, the generalized additive models for location, scale and shape (GAMLSSs) also allow different types of random effects to be included in the model formulation. Even though already popular in many areas of research, this type of models have not been found to be used for mixed modeling purposes yet. Therefore, this paper describes the analysis of an experiment with plants' growth using mixed GAMLSSs, comparing it to a linear mixed model approach.

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Available abstract

The standard procedures for analysing hierarquical or grouped data are by (non)linear mixed models or generalized mixed models. However, the generalized additive models for location, scale and shape (GAMLSSs) also allow different types of random effects to be included in the model formulation. Even though already popular in many areas of research, this type of models have not been found to be used for mixed modeling purposes yet. Therefore, this paper describes the analysis of an experiment with plants' growth using mixed GAMLSSs, comparing it to a linear mixed model approach.

Key concepts: Mixed model, Generalized linear mixed model, Random effects model, Multilevel model, Generalized additive model, Scale (ratio), Linear model, Additive model

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