期刊
ECOLOGY AND EVOLUTION
卷 6, 期 19, 页码 7047-7056出版社
WILEY
DOI: 10.1002/ece3.2449
关键词
excessive zeros; habitat preference; hierarchical generalized linear model; pellet-group count; Poisson model; spatial correlation
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [2011-0030810]
- Brain Research Program through NRF - Ministry of Science, ICT and Future Planning [2014M3C7A1062896]
- Swedish Agency of Energy
- research programme Vindval [31940-1]
We analyze a real data set pertaining to reindeer fecal pellet-group counts obtained from a survey conducted in a forest area in northern Sweden. In the data set, over 70% of counts are zeros, and there is high spatial correlation. We use conditionally autoregressive random effects for modeling of spatial correlation in a Poisson generalized linear mixed model (GLMM), quasi-Poisson hierarchical generalized linear model (HGLM), zero-inflated Poisson (ZIP), and hurdle models. The quasi-Poisson HGLM allows for both under-and overdispersion with excessive zeros, while the ZIP and hurdle models allow only for overdispersion. In analyzing the real data set, we see that the quasi-Poisson HGLMs can perform better than the other commonly used models, for example, ordinary Poisson HGLMs, spatial ZIP, and spatial hurdle models, and that the underdispersed Poisson HGLMs with spatial correlation fit the reindeer data best. We develop R codes for fitting these models using a unified algorithm for the HGLMs. Spatial count response with an extremely high proportion of zeros, and underdispersion can be successfully modeled using the quasi-Poisson HGLM with spatial random effects.
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