Introducing COZIGAM: An R Package for Unconstrained and Constrained Zero-Inflated Generalized Additive Model Analysis

• Main Authors: Hai Liu, Kung-Sik Chan
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2010-10-01
• Series: Journal of Statistical Software
• Subjects: EM algorithm; model selection; penalized likelihood; proportionality constraints
• Online Access: http://www.jstatsoft.org/v35/i11/paper

Zero-inflation problem is very common in ecological studies as well as other areas. Nonparametric regression with zero-inflated data may be studied via the zero-inflated generalized additive model (ZIGAM), which assumes that the zero-inflated responses come from a probabilistic mixture of zero and a regular component whose distribution belongs to the 1-parameter exponential family. With the further assumption that the probability of non-zero-inflation is some monotonic function of the mean of the regular component, we propose the constrained zero-inflated generalized additive model (COZIGAM) for analyzingzero-inflated data. When the hypothesized constraint obtains, the new approach provides a unified framework for modeling zero-inflated data, which is more parsimonious and efficient than the unconstrained ZIGAM. We have developed an <b>R</b> package <b>COZIGAM</b> which contains functions that implement an iterative algorithm for fitting ZIGAMs and COZIGAMs to zero-inflated data basedon the penalized likelihood approach. Other functions included in the package are useful for model prediction and model selection. We demonstrate the use of the <b>COZIGAM</b> package via some simulation studies and a real application.