Zero-inflated poisson coefficients PreviousNext
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 Tracy Witte posted on Thursday, August 26, 2010 - 8:50 pm
I am very confused about how to interpret the coefficients for zero-inflated poisson regressions. For each regression, you get coefficients predicting the preponderance of zeroes and for the count predictors. To interpret them, do you take the anti-log of the coefficients and interpret them as an odds ratio for increasing the variable of interest by 1 unit? I've done many web searches and I seem to find conflicting information on this. Any guidance would be greatly appreciated.

I've pasted some of my output below. As you can see, some of the estimates are negative. I don't understand what this means...(and do you interpret the coefficients for the preponderance of zeroes differently than the coefficients for the count variable?)

Estimate S.E. Est./S.E. P-Value

X1 1.692 0.455 3.715 0.000
X2 0.221 0.266 0.831 0.406

Y#1 ON
X1 2.807 1.685 1.666 0.096
X2 -0.059 0.746 -0.079 0.937
 Bengt O. Muthen posted on Thursday, August 26, 2010 - 9:46 pm
For the Y# ON equation you are using a standard logistic regression with a binary dependent variable (being in the zero class or not). So even though this binary DV is latent, the usual rules apply in terms of odds ratios. A negative slope simply means that the odds ratio is lower for being in the zero class when x2 increases.

For the Y ON equation, things are a little different because the DV relates to counts. Look at Scott Long's book that we refer to in the UG. His pages 223-226 are relevant. The factor change on p. 225 uses exp (i.e. anti-log), but the wording is different from that of logistic regression.
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