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Dear Linda and Bengt, I am trying to run a zeroinflated poisson model for a count variable (cigarette use), using 35 points of assessment. I attempted to estimate a model using example 6.7 in the manual, however the model failed to estimate. My questions are the following: 1) Since I am only estimating an intercept model (no slope), is the example 6.7 applicable for me? I deleted "s" and "si" as well as "s@0" and "si@0" in the Model statement. Is there anything else I need to adjust if I want to estimate an intercept only model? 2) I was also considering estimating this model using example 6.16 of the manual. However, it appears very similar to the model in the example 6.7. What are the differences between these two models? And which one would you recommend I use for my case (i.e., an intercept only model for a cigarette use variable, assessed weekly over a course of a semester; 35 points of assessment)? Thank you very much for your help. 


1) The way you are doing it is correct. You might want to first try a noninflated Poisson model. If you have problems, send your materials and license number to support@statmodel.com. 2)ex 61.6 is a twopart model. This is a little different than ZIP. ZIP is a 2class model where there are 2 classes that can produce a zero value, while twopart model is a singleclass model. With twopart you would have to treat the positive number of cigarettes as continuouslognormal; the count option should not be used because Mplus does not provide a truncated (at zero) Poisson. If you have most people at zero, you may want to use ZIP. 

Qiana Brown posted on Wednesday, June 25, 2014  7:04 am



Hello, 1. Can I use the growth model for parallel processes if my outcomes are not continuos? I have one outcome (past month smoking) that is binary, and another outcome that is continuous. I would like to model them using the parallel process growth model, but section 6.13 in the User's guide only mentions the parallel process for continuous outcomes. 2. My binary smoking variable has several zeros at each time point. About 82% of the participants are zeros (did not smoke in the past month). Should I used a zeroinflated poisson growth model in this case, or will a growth model for binary outcomes suffice? Also, can the zeroinflated poisson growth model be modeled in the parallel processes framework? Thank you 


1. Yes. A binary and a cont's process can be handled. 2. You don't want to use Poisson if you have only 2 response categories. Only if you smoking variable really consists of counts. Yes, you can combine a zIP model for one of the parallel processes with a cont's variable process. 


Hello, I used a parallel processes growth model with a MLR estimator to model a binary and continuous outcome. Should the estimates be interpreted as if both outcomes were continuous? Also, are there any specific model fit indices that I should consider in this case? Would you please recommend a paper that might help me? Thank you 


The analysis and interpretation of binary growth models is a large topic that we cover in Topic 3 of our short courses on our website. See the video and handout, slides 185212. This also gives references. See also the paper on our website under Papers, Growth Modeling: Masyn, K., Petras, H. and Liu, W. (2013). Growth Curve Models with Categorical Outcomes. In Encyclopedia of Criminology and Criminal Justice (pp. 20132025). Springer. 


Thank you. I appreciate the resources. 

Qiana Brown posted on Wednesday, July 09, 2014  11:04 am



Hello, The slides and lecture for Topic 3 were helpful. I still have a question regarding the interpretation of the estimates from my parallel processes growth model. In my model, one outcome is binary and the other continuous. The estimator is MLR and the link is logit  so are the slopes log odd ratios and the thresholds negative log odds? Thank you 


Right. 

JLuk posted on Wednesday, November 11, 2015  12:01 pm



I'm running a ZIP latent growth model for a count outcome, number of alcohol problems, in a 7wave data. The proportion of zeros is about 4550% across all waves and the BIC is lower for the ZIP vs. Poisson latent growth model. As a next step, I'm interested in looking at timeinvariant (baseline) covariate effects. I regressed i, s, ii and si on the covariates. The majority of the results make sense except that I found two unexpected effects on the slope of the zeroinflated part, and so I wanted to make sure I'm interpreting the results correctly. (1) From what I understand, a covariate with a positive coefficient on the zeroinflated intercept means that this covariate is associated with greater log odds of having zero alcohol problem at baseline. Is this correct? (2) By extension, does a covariate with a positive coefficient on the zeroinflated slope means that this covariate is associated with greater log odds of remaining to have zero alcohol problems over time? (3) If so, then I have results that are seemingly contradictory. Specifically, one (same) covariate is positively associated with the slope of alcohol problems in the count part (i.e., growth of alcohol problems over time), but is also positively associated with the slope of alcohol problems in the zeroinflation part (i.e., greater log odds of remaining to have zero alcohol problems over time?). Is this possible? 


(1) Right. (2) Yes. (3) It is possible. The covariate increases the probability of not drinking over time, but among those who drink, it increases the amount they drink. 

anonymous Z posted on Thursday, September 07, 2017  1:01 pm



Dear Dr. Muthen, Is zero inflated poisson a type of mixture modeling? Thanks so much! 


It is a model for count variables. 

anonymous Z posted on Thursday, September 07, 2017  1:37 pm



Hi Linda, Thanks for your prompt reply. I understand that ZIP is for count variables. Given it is a twoclass model, I wondered if it is a type of mixture modeling. Thanks! 


Yes, it can also be seen as a mixture model. We give that alternative representation of the ZIP model in UG ex 7.25. 

Onsen Juiko posted on Tuesday, October 03, 2017  3:57 pm



Following UG6.7, I fit a linear latent growth curve model using a zeroinflated Poisson model to the data (drug use at three different time points). Given that the binary part of the estimated threshold was 0.70 (which was fixed to be equal across times), could I get a probability of being 'nondrug users' at time 1 in the following way? exp(0.70)/[1+exp(0.70)] = 0.33 If this was correct, I am a bit confused because the observed count proportion of zero at time 1, which indicates 'nondrug users,' was way larger (0.65). Are those two values supposed to be closer each other? Thank you in advance for your input. 


That's just part of the prob of a zero count. Don't forget that ZIP has "a mixture at zero". The other part is the prob of a zero count that comes from the regular Poisson. See slide 69 of our Topic 2 handout for our short courses on our website at http://www.statmodel.com/course_materials.shtml 

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