could somebody tell me if MPLUS is able to combine poisson distributed count data variables (independent as well as dependent) and normal distributed variables in one path model with observed variables? many thanks in advance, joerg.
In path analysis, observed outcome variables can be continuous, censored, binary, ordered categorical (ordinal), counts, or combinations of these variable types. In addition, for path analysis for non-mediating outcomes, observed outcomes variables can be unordered categorical (nominal). Observed independent variables can be binary or continuous.
Hi, I have run a path analysis on a longitudinal dataset which combines continuous and count data, treated with Poisson regression and zero-inflated Poisson regression. After comparing a few alternative models, I have choosen the best model (the lowest BIC). Now, I would like to cross-validate this model on a second dataset, but I have 2 doubts: 1) How can I evaluate the goodness of the model for the cross-validation when Poisson regression is involved? I guess that I cannot use the traditional tests of model fits... 2) What is the syntax for the cross-validation? Are there some specific aspects that I should take into account? The syntax for my model follows: VARIABLE: NAMES ARE y1-y6 u1-u3 x1-x2; USEVARIABLES ARE y1-y6 u1-u2 x1 x2; COUNT IS u1; COUNT IS u2 (i);
MODEL: y1 on x1 x2; y2 on x1 y1 u1; y3 on x1 y1 y2 u2; y4 on x1 x2; y5 on y4 x2 u1; y6 on y4 y5 x2 u2; u1 on x1 x2; u2 on x1 x2; u2#1 on x1 x2; y1 y2 y3 (3); y4 y5 y6 (4); y6 with y3@0; y6 with y1@0; y6 with y2@0; y1 with y3@0; y2 with y1@0; y2 with y3@0;
Thank you very much for your time and support. michela
We are running an SEM model in which we want to use zero inflated poisson regression to predict each of 4 count variable criteria. We have checked each count variable to ensure that each has only integer values. Each time we try to run the analysis, we get an error message saying "There is at least one observation in the data set where a count variable has negative or non-integer values." None of the 4 variables shows anything but 0, 1, 2, 3, 4, or 5. We cannot fix it and need help. Thank you.
It sounds like you have blanks in the data set. This is not allowed with free format data. If you can't figure it out, please send the input, data, output, and your license number to firstname.lastname@example.org.
sahar shadi posted on Wednesday, August 22, 2012 - 11:21 pm
Dear all, I am now constructing a SEM model with 1 exogenous variable and 1 final endogenous variable . In between there are 5 key mediators (2 latent and 3 observed). The type of my endegenous variable is count. I used AMOS18 but I know I have to do this analysis with MPLUS Is my analysis completly wrong ? or is better to do with mplus? I am new begginer. please help me thank you very much
I am running a model in which I have a count predictor (IV), and continuous mediators and outcomes. Can I use COUNT = IV in Mplus, when the predictor is count (and not the DV)? Would it be appropriate to specify a predictor as a count variable?
Two quick questions: Is there any problem using a count variable as a mediator? If there is no problem, it should also be possible to build a simple markov chain with several count variables measured in different time point, right?
A count mediator (M) presents a problem as I see it. In the regression
M ON X;
we can specify M as count and do a Poisson regression. But what do we do with M in the regression
Y on M;
? ML estimation in Mplus would treat M as continuous which contradicts the first regression. There is no underlying latent response variable M* for counts so WLSMV and Bayes can't use that approach. I see it as an unresolved research area.
I'm trying to perform what I believe is a simple analysis. I have a latent variable predicting a count variable and I want to examine group differences. From what I've seen, I have to use the KNOWNCLASS command, but I can't seem to figure out how to satisfy all of the requirements to get the analysis to run.
This is the model I *want* to run:
USEVARIABLES ARE g2choice ind1-ind3; MISSING IS .; GROUPING = GROUP (1 = g1 2 = g2 3 = g3); COUNT = outcome;
MODEL: FAC by ind1-ind3; outcome on FAC;
Could you clarify how I should fix my syntax? Also, with the corrected syntax, will I be able to constrain the ON path across groups to test for differences? I imagine that it won't be quite the same as using the MODEL subgroup commands.
i want to run a SEM with two dependent count variables which are zero-inflated and at least five independent continuous variables. So I use the zero-inflated poisson regression as in example 3.8. If I use more as two independent variables for one dependent count variable, then the Error Message in the output is:
“COUNT VARIABLE HAS LARGE VALUES. IT MAY BE MORE APPROPRIATE TO TREAT SUCH VARIABLES AS CONTINUOUS”
My Questions are:
1.) If possible, how can I transform zero-inflated dependent count variables in continuous (with the poisson assumption)?
2.) Do I need a pc with more power?
3.) In another post with the same error I have read that it’s possible to use the two-part model. But this was in the case of a LGM, so I’m not sure, if this solution also works in my case?
Sounds like there is something off in the input related to data reading. One way to check that your input is correct is to use the Savedata command to see that the analysis variables contain what you expect.
Sarah Arpin posted on Friday, February 23, 2018 - 4:18 pm
Thank you for your fast response and your suggestion. I checked the input file using the Savedata command and all looks fine. I also opened the file within the Mplus Editor, and deleted the strange character at the beginning of the file, resaved, and still received the same error message. There are no blanks in the file. Do you have any other recommendations?
Count DVs don't have a residual so standardizing with respect to such a DV doesn't really make sense. You can standardized wrt to predictors of such a DV and the growth factors are such predictors. STD standardizes wrt factors including growth factors. That's the reasoning.
I have an independent variable which is a count variable (v) in my model. Everything I have read about count variables in mplus, is about dependent count variables not independent count variables. v's kurtosis and skewness are 3 and 1.4. When i use "COUNT is v(i);" the results and fit indices are different. The question is, when the count variable is not the dependent variable in a model, should I use the statement "COUNT is v(i);", or just run the analysis with MLR and treat v like other variables? any comment would be appreciated. cheers
Count as an IV is problematic. There isn't an underlying continuous latent response variable that you can use for linearly predicting a DV from it. If you treat it as just another continuous predictor, you have to ask if the scores 0, 1, 2,.. are meaningful for producing a linear prediction of a DV.
Thanks a lot. I totally agree with you and will try to find a way to make the distribution of the count variable more similar to a normal distribution. Maybe using transformations. but this count variable is an independent variable in a large model.
If I include "COUNT is v(i);", will a zero-inflated Poisson regression be estimated? if yes, this is a problem, because my outcome variable is a continuous variable and I need a normal regression.
1- so one question is, what will happen in mplus if I an independent variable is specified to be a count variable not a dependent variable?
2- is there a way to tell mplus that an independent variable is a count variable (and mplus takes this into account), but still run a normal regression?