The analysis of zero-inflated count data: Beyond zero-inflated Poisson regression.
The analysis of zero-inflated count data: Beyond zero-inflated Poisson regression.
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DOI：
10.1111/j.2044-8317.2011.02031.x
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发表时间：
2012-02-01
影响因子：
2.6
通讯作者：
Buysse, Ann
中科院分区：
文献类型：
--
作者：
Loeys, Tom;Moerkerke, Beatrijs;Buysse, Ann
心理学研究中不常见的计数数据通常使用零膨胀泊松回归进行建模。该模型可以被视为始终为零分量和泊松分量的潜在混合。跨栏模型是一类双分量模型的替代类，很少在心理学研究中使用，但通过对后者使用左截断计数模型来清楚地区分零计数和非零计数。在本教程中，我们重新审视这两类模型，并讨论模型比较及其参数的解释。正如关系心理学的一个例子所示，这两种类型的模型都可以使用 R 包 pscl 轻松拟合。
Infrequent count data in psychological research are commonly modelled using zero-inflated Poisson regression. This model can be viewed as a latent mixture of an always-zero component and a Poisson component. Hurdle models are an alternative class of two-component models that are seldom used in psychological research, but clearly separate the zero counts and the non-zero counts by using a left-truncated count model for the latter. In this tutorial we revisit both classes of models, and discuss model comparisons and the interpretation of their parameters. As illustrated with an example from relational psychology, both types of models can easily be fitted using the R-package pscl.