Negative binomials? PreviousNext
Mplus Discussion > Categorical Data Modeling >
Message/Author
 Andy Cohen posted on Monday, July 09, 2007 - 6:56 am
I just recently purchased version 4.21. I don't see mention of negative binomials in the user guide but wanted to check here to see whether Mplus has the functionality to model a negative binomial distribution for my ultimate outcome variable. What can you tell me?
 Linda K. Muthen posted on Monday, July 09, 2007 - 7:28 am
Mplus does not model a negative binomial distribution at this time. Mplus does model the zero-inflated Poisson distribution.
 mpduser1 posted on Thursday, November 15, 2007 - 12:48 pm
Does Mplus 4.21 not provide an R-Square for Poisson models ?

It seems there a "space" for it on the output, but that no value is reported.

Thanks.
 Linda K. Muthen posted on Thursday, November 15, 2007 - 1:25 pm
The residual variance for a count variable is not defined so this value is not available.
 Susan E. Collins posted on Tuesday, February 17, 2009 - 1:43 am
Hi there,

In my cross-lagged path analysis, i have dvs that are nb distributed. I added the line "COUNT ARE u1 (nb) u2 (nb) u3 (nb) u4 (nb) u5 (nb);" as well as" ANALYSIS: INTEGRATION=MONTECARLO;"

but the output shows a fatal error:

"FATAL ERROR NEGATIVE BINOMIAL VARIABLE HAS IMPUTED VALUE GREATER THAN 50000."

it does this with different dvs i enter as well.

could you please tell me what this might mean?

Thanks!
Susan
 Linda K. Muthen posted on Tuesday, February 17, 2009 - 10:46 am
Please send your input, data, output, and license number to support@statmodel.com. I'm not sure exactly what would cause that error message.
 Dustin Pardini posted on Friday, May 06, 2011 - 6:13 am
I am conducting a simple negative binomial regression that only has missing data on the predictors, which are both categorical and continuous. Is Mplus able to use all available data to estimate parameters from this model?
 Linda K. Muthen posted on Friday, May 06, 2011 - 7:08 am
For any model, missing data theory does not apply to the observed exogenous variables. The model is estimated conditioned on these variables and any observation with missing on one or more of these variables is eliminated from the analysis. You can bring these variables into the model by mentioning their variances in the MODEL command. When you do this, they are treated as endogenous variables and distributional assumptions are made about them.
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