A Statistical Model for Analyzing Interdependent Complex of Plant Pathogens

• Main Authors: EDUARDO DÁVILA, LUIS ALBERTO LÓPEZ, LUIS GUILLERMO DÍAZ
• Format: Article
• Language: English
• Published: Universidad Nacional de Colombia 2012-06-01
• Series: Revista Colombiana de Estadística
• Subjects: Epidemiological methods; Extra-binomial variation; Multivariate data
• Online Access: http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-17512012000200005&lng=en&tlng=en

We introduce a new approach for modeling multivariate overdispersed binomial data, from a plant pathogen complex. After recalling some theoretical foundations of generalized linear models (GLMs) and Copula functions, we show how the later can be used to model correlated observations and overdispersed data. We illustrate this approach using fungal incidence in vegetables, which we analyzed using Gaussian copula with Beta-binomial margins. Compared to classical and generalized linear models, the model using Gaussian copula function best controls for overdispersion, being less prone to the underestimation of standard errors, the major cause of wrong inference in the statistical analysis of plant pathogen complex.