Please excuse me if this has been answered already, but I couldn't tell from the threads.
We want to use ZINB because we have a highly non-normal count distribution (of mental health visits) with a very large number of 0's and standard deviation much larger than the mean. We believe that ZINB is the best way to handle this.
Thank you both for your responses! I will review these this week!
LAS posted on Tuesday, September 28, 2010 - 12:28 pm
Hello. I am currently running LCGA and GMM models using highly skewed data with a large percentage of 0s. I explored using four different models: the Poisson, Zero-inflated Poisson (ZIP), negative binomial, and Zero-inflated Negative Binomial (ZINB). Compared to the NB and ZINB, the Poisson and ZIP performed poorly (based on the BICs), so I eliminated these models from consideration. When I ran the ZINB, mplus set the logit parameters for all classes to -15 (regardless of how many classes I extracted). As a result, the ZINB seemed to reduce to the NB with the ZINB and the NB producing identical log likelihoods. Based on these results, can I assume that the inflation parameters are not needed and that the most appropriate model is the NB? Thank you!
Yes. I assume you have multiple classes as well. That sometimes removes the need for zero-inflation, at least for NB which already picks up the preponderance of zeros to some extent - at least better than Poisson.
I don't think of FIML as being in operation when you have only a single dependent variable as you do in those regression models. Dealing with missing on the DV is simply the same as deleting the subject because it has no information on the relationship between the DV and covariates, nor on the DV. Dealing with missing on covariates goes beyond the regression model.
For FIML - that is ML under MAR - to play a role you need more than one DV so that missing on one of them borrows information from the other.
I am running a zero-inflated Poisson LCA model with three count outcomes that measure the number of days of prescription drug use (stimulants, pain killers, and sedatives) in the prior 3 months. I have three questions regarding this analysis:
1. If I have a variable that could indicate true structural zeros (i.e., we measured whether or not participants had ever used each class of drugs in their lives), would it be better to include those as class indicators than to run a zero-inflated model?
2. Can you briefly explain the difference between the default of fixing the inflation parameters across classes versus freeing them? I'm having trouble finding a reference that would assist me in the relative interpretation of the two.
3. The model is giving me a message under the model fit section saying "** Large values were truncated at 9." Does this mean that the analysis truncates all values (which range all the way to a value of 90) to 9 for the purpose of analysis, or just for the purpose of computing chi-square statistics? Can you point me to any references on the appropriateness and interpretability of this and how this affects the sample and estimated means for the count variables?