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From |
David Hoaglin <dchoaglin@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Zero Inflated Negative Binomial model |

Date |
Sun, 22 Jan 2012 09:26:34 -0500 |

The exploratory step that I sketched earlier is closely related to a hurdle model. A brief discussion in the book by Agresti (2010) cites papers by Saei et al. (1996) and Min and Agresti (2005). For the nonzero categories a cumulative logit model might work (as in ordinal logistic regression), and you could try other cumulative link functions. References Agresti, A (2005). Analysis of Ordinal Categorical Data, second edition. Wiley. Min Y, Agresti A (1996). Random effect models for repeated measures of zero-inflated count data. Statistical Modeling 5:1-19. Saei A, Ward J, McGilchrist CA (1996). Threshold models in a methodone programme evaluation. Statistics in Medicine 15:2253-2260. David Hoaglin On Sat, Jan 21, 2012 at 8:06 AM, David Hoaglin <dchoaglin@gmail.com> wrote: > Eugene, > > You are correct that using a ZINB model would be problematic. The NB > distribution applies to counted data (i.e., it is possible for any > nonnegative count to occur in the outcome variable). When you have > only categories, that requirement is not satisfied, no matter what > value you choose to represent each category. > > I don't know whether the ordinal logit model has a zero-inflated > version (I have not searched). Here "zero-inflated" would mean that > the first category is inflated, since numerical values associated with > the ordered categories are only labels. If someone has worked out > such a model, you would still need to determine whether, in your data, > the assumption of proportional odds is reasonable. You could try an > ordinal logistic regression model with your data as they stand, and > see what happens. > > As an exploratory step, you could fit a binary logit model to "0 > times" versus "1 or more times"; that would address the question of > crossing the threshold into self-injurious behavior. You could then > work with only the nonzero categories and dichotomize the outcome > variable at each of the category boundaries (or some of them) and fit > a binary logit model to each dichotomized outcome. Comparison of the > coefficients on the predictor variables among those models would give > you an indication of whether the proportional odds model is > reasonable. > > You didn't describe the sorts of predictor variables that you have. > Other analytic approaches may be possible. > > David Hoaglin * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Zero Inflated Negative Binomial model***From:*Eugene Walls <Eugene.Walls@du.edu>

**Re: st: Zero Inflated Negative Binomial model***From:*David Hoaglin <dchoaglin@gmail.com>

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