Zero-Inflated Discrete Statistical Models for Fecundity Data Analysis in Channel Catfish, Ictalurus punctatus
2007
Quintero, Herbert E. | Abebe, Asheber | Davis, D Allen
Traditional fecundity analysis, either as number of eggs per body weight of female or number of eggs per gram of egg mass, pay little attention to females that do not spawn. These fecundity variables contain a high proportion of zeroes either because of the absence of eggs or the inability to recover the eggs. Zero-inflated discrete generalized linear models are an alternative method that can be developed to take into account females that do not spawn. In this case study, we propose discrete generalized linear models that use specially constructed mixture models to handle the excess zeroes such as the zero-inflated Poisson and zero-inflated negative binomial models. These models have the advantage of modeling fecundity simultaneously with the probability of spawning. The results show that age was the most significant factor influencing the number of eggs per gram of egg mass, while period of spawning was the most significant factor influencing the number of eggs per female body weight. These were also the most important variables that significantly affected the probability of successful spawning. Model residual diagnostics show that zero-inflated models exhibit superior performance compared to the traditional models like analysis of covariance, Poisson regression, and negative binomial regression models.
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