Generalized additive models for location, scale and shape in the analysis of common bean productivity
DOI:
https://doi.org/10.5902/2179460X85223Keywords:
Distributional regression models, Model selection, Phaseolus vulgaris LAbstract
The common bean (Phaseolus vulgaris L.) is a leguminous plant and one of the world’s most important crops, with substantial economic relevance. Hence, the main aim of this paper is to analyze the productivity of common beans, establishing relationships with specific variables. For this purpose, the following candidate explanatory variables were considered: plant height, number of branches per plant, days to flowering, days to maturity, number of seeds per pod, number of pods per plant, and seed mass. Because of its flexibility in explaining the behaviour of a response variable, the generalized additive models for location, scale, and shape (GAMLSS) were used for statistical modelling. Initially, three distinct distributions for the response variable (productivity) were considered: the inverse gamma (IGAMMA), the generalized gamma (GG), and the inverse Gaussian (IG). The covariates for the regression structures were selected using the so-called Strategy A, a stepwise-based method. Based on both Akaike and Schwarz criteria, the GAMLSS based on the IG distribution was chosen as the best fit. The variables number of pods per plant and days to maturity had a positive significant effect on average productivity, whereas the number of branches per plant presents a negative effect on its variability. Based on a residual analysis, we can conclude that the fitted GAMLSS based on the IG distribution is appropriate to explain the data.
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