Poisson Counts

2 replies [Last post]
rabil's picture
Joined: 01/14/2010

I've asked about this before but I don't think I ever received any replies. Can OpenMx handle Poisson counts as observed variables? I have counts that are too small to treat as continuous Normally distributed outcomes and I need a way to model them. Any insights would be greatly appreciated.

salarim1's picture
Joined: 05/07/2014
poisson gmm

I want to add poisson likelihood function in growth mixture model for dependent variable.I need to model count variable.
Any insights would be greatly appreciated.

RobK's picture
Joined: 04/19/2011
Yes, but it's not built-in

Yes, OpenMx can utilize the Poisson likelihood function, but it is not built-in. You will have to set up your model using an algebra objective or an R objective. See my examples below. Note that if your count variable takes relatively few unique values in your sample, you could consider ordinalizing it and doing the analysis as an ordinal thresholds model instead.

Let's first simulate some data for some simple examples:

dat <- cbind( rpois(1000,1), 1, rnorm(1000) )
dimnames(dat) <- list(NULL,c("y","x0","x1"))

If we want to do a simple Poisson regression, setting up the model to use a row-algebra objective would look something like this:

PoisAlgModel <- mxModel(
  mxAlgebra( exp(xt%*%b), name="yhat", dimnames=list(NULL,"y")),
  mxAlgebra( -2*filteredDataRow*log(yhat) + 2*yhat, name="rowAlgebra" ),
  mxAlgebra( sum(rowResults), name="reduceAlgebra" ),
PoisAlgFit <- mxRun(PoisAlgModel)

If we want to do the same analysis, but with an R objective, the code would look something like this:

Poisobj <- function(model,state){
  modeldata <- model$data@observed
  b0 <- model$b@values[1,1]
  b1 <- model$b@values[2,1]
  return(-2*sum(apply(modeldata,1,function(row){dpois(x=row[1],lambda=exp(b0*row[2] + b1*row[3]),log=T)})))
PoisRModel <- mxModel(
PoisRFit <- mxRun(PoisRModel)

Note that the two models' likelihood functions are not equal, but merely proportional. So, the parameter estimates should be the same (or very very close), but the value of the -2loglikelihood won't be. It sounds as though you have a longitudinal mixture analysis in mind, so naturally, your objective function will be way more complicated than the ones I demonstrated here, but I hope I've given enough to get you started. I might be able to help you more if you say more about the analysis you want to do.

Finally, I feel I certainly should warn you about the problem of overdispersion, in case you don't know. If your count variable's variance appreciably exceeds its mean, even after conditioning on covariates, you should not trust the standard errors, confidence intervals, and likelihood-ratio tests from models using the classic Poisson likelihood. You would need to adjust those inferential results appropriately, or use a different objective function. The point estimates of your regression coefficients won't be biased by the overdispersion, though.