The following example shows how you can use PROC FMM to model data with more zero values than expected.
Many count data show an excess of zeros relative to the frequency of zeros expected under a reference model. An excess of zeros leads to overdispersion since the process is more variable than a standard count data model. Different mechanisms can lead to excess zeros. For example, suppose that the data are generated from two processes with different distribution functions—one process generates the zero counts, and the other process generates nonzero counts. In the vernacular of Cameron and Trivedi (1998), such a model is called a hurdle model. With a certain probability—the probability of a nonzero count—a hurdle is crossed, and events are being generated. Hurdle models are useful, for example, to model the number of doctor visits per year. Once the decision to see a doctor has been made—the hurdle has been overcome—a certain number of visits follow.
Hurdle models are closely related to zeroinflated models. Both can be expressed as twocomponent mixtures in which one component has a degenerate distribution at zero and the other component is a count model. In a hurdle model, the count model follows a zerotruncated distribution. In a zeroinflated model, the count model has a nonzero probability of generating zeros. Formally, a zeroinflated model can be written as




where is a standard count model with mean and support .
The following data illustrates the use of a zeroinflated model. In a survey of park attendees, randomly selected individuals were asked about the number of fish they caught in the last six months. Along with that count, the gender and age of each sampled individual was recorded. The following DATA step displays the data for the analysis:
data catch; input gender $ age count @@; datalines; F 54 18 M 37 0 F 48 12 M 27 0 M 55 0 M 32 0 F 49 12 F 45 11 M 39 0 F 34 1 F 50 0 M 52 4 M 33 0 M 32 0 F 23 1 F 17 0 F 44 5 M 44 0 F 26 0 F 30 0 F 38 0 F 38 0 F 52 18 M 23 1 F 23 0 M 32 0 F 33 3 M 26 0 F 46 8 M 45 5 M 51 10 F 48 5 F 31 2 F 25 1 M 22 0 M 41 0 M 19 0 M 23 0 M 31 1 M 17 0 F 21 0 F 44 7 M 28 0 M 47 3 M 23 0 F 29 3 F 24 0 M 34 1 F 19 0 F 35 2 M 39 0 M 43 6 ;
At first glance, the prevalence of zeros in the DATA set is apparent. Many park attendees did not catch any fish. These zero counts are made up of two populations: attendees who do not fish and attendees who fish poorly. A zeroinflation mechanism thus appears reasonable for this application since a zero count can be produced by two separate distributions.
The following statements fit a standard Poisson regression model to these data. A common intercept is assumed for men and women, and the regression slope varies with gender.
proc fmm data=catch; class gender; model count = gender*age / dist=Poisson; run;
Figure 37.7 displays information about the model and data set. The “Model Information” table conveys that the model is a singlecomponent Poisson model (a Poisson GLM) and that parameters are estimated by maximum
likelihood. There are two levels in the CLASS variable gender
, with females preceding males.
Figure 37.7: Model Information and Class Levels in Poisson Regression
Model Information  

Data Set  WORK.CATCH 
Response Variable  count 
Type of Model  Generalized Linear (GLM) 
Distribution  Poisson 
Components  1 
Link Function  Log 
Estimation Method  Maximum Likelihood 
Class Level Information  

Class  Levels  Values 
gender  2  F M 
Number of Observations Read  52 

Number of Observations Used  52 
The “Fit Statistics” and “Parameter Estimates” tables from the maximum likelihood estimation of the Poisson GLM are shown in Figure 37.8. If the model is not overdispersed, the Pearson statistic should roughly equal the number of observations in the data set minus the number of parameters. With n=52, there is evidence of overdispersion in these data.
Figure 37.8: Fit Results in Poisson Regression
Fit Statistics  

2 Log Likelihood  182.7 
AIC (smaller is better)  188.7 
AICC (smaller is better)  189.2 
BIC (smaller is better)  194.6 
Pearson Statistic  85.9573 
Parameter Estimates for 'Poisson' Model  

Effect  gender  Estimate  Standard Error  z Value  Pr > z 
Intercept  3.9811  0.5439  7.32  <.0001  
age*gender  F  0.1278  0.01149  11.12  <.0001 
age*gender  M  0.1044  0.01224  8.53  <.0001 
Suppose that the cause of overdispersion is zeroinflation of the count data. The following statements fit a zeroinflated Poisson model.
proc fmm data=catch; class gender; model count = gender*age / dist=Poisson ; model + / dist=Constant; run;
There are two MODEL statements, one for each component of the mixture. Because the distributions are different for the components, you cannot
specify the mixture model with a single MODEL statement. The first MODEL statement identifies the response variable for the model (count
) and defines a Poisson model with intercept and genderspecific slopes. The second MODEL statement uses the continuation operator (“+”) and adds a model with a degenerate distribution by using DIST=CONSTANT. Because the mass of the constant is placed by default at zero, the second MODEL statement adds a zeroinflation component to the model. It is sufficient to specify the response variable in one of the MODEL statements; you use the “=” sign in that statement to separate the response variable from the model effects.
Figure 37.9 displays the “Model Information” and “Optimization Information” tables for this run of the FMM procedure. The model is now identified as a zeroinflated Poisson (ZIP) model with two components, and the parameters continue to be estimated by maximum likelihood. The “Optimization Information” table shows that there are four parameters in the optimization (compared to three parameters in the Poisson GLM model). The four parameters correspond to three parameters in the mean function (intercept and two genderspecific slopes) and the mixing probability.
Figure 37.9: Model and Optimization Information in the ZIP Model
Model Information  

Data Set  WORK.CATCH 
Response Variable  count 
Type of Model  Zeroinflated Poisson 
Components  2 
Estimation Method  Maximum Likelihood 
Optimization Information  

Optimization Technique  Dual QuasiNewton 
Parameters in Optimization  4 
Mean Function Parameters  3 
Scale Parameters  0 
Mixing Prob Parameters  1 
Number of Threads  2 
Results from fitting the ZIP model by maximum likelihood are shown in Figure 37.10. The –2 log likelihood and the information criteria suggest a muchimproved fit over the singlecomponent Poisson model (compare Figure 37.10 to Figure 37.8). The Pearson statistic is reduced by factor 2 compared to the Poisson model and suggests a better fit than the standard Poisson model.
Figure 37.10: Maximum Likelihood Results for the ZIP model
Fit Statistics  

2 Log Likelihood  145.6 
AIC (smaller is better)  153.6 
AICC (smaller is better)  154.5 
BIC (smaller is better)  161.4 
Pearson Statistic  43.4467 
Effective Parameters  4 
Effective Components  2 
Parameter Estimates for 'Poisson' Model  

Component  Effect  gender  Estimate  Standard Error  z Value  Pr > z 
1  Intercept  3.5215  0.6448  5.46  <.0001  
1  age*gender  F  0.1216  0.01344  9.04  <.0001 
1  age*gender  M  0.1056  0.01394  7.58  <.0001 
Parameter Estimates for Mixing Probabilities  

Effect  Linked Scale  Probability  
Estimate  Standard Error  z Value  Pr > z  
Intercept  0.8342  0.4768  1.75  0.0802  0.6972 
The number of effective parameters and components shown in Figure 37.8 equals the values from Figure 37.9. This is not always the case because components can collapse (for example, when the mixing probability approaches zero or when two components have identical parameter estimates). In this example, both components and all four parameters are identifiable. The Poisson regression and the zero process mix, with a probability of approximately 0.6972 attributed to the Poisson component.
The FMM procedure enables you to fit some mixture models by Bayesian techniques. The following statements add the BAYES statement to the previous PROC FMM statements:
proc fmm data=catch seed=12345; class gender; model count = gender*age / dist=Poisson; model + / dist=constant; performance cpucount=2; bayes; run;
The “Model Information” table indicates that the model parameters are estimated by Markov chain Monte Carlo techniques, and it displays the random number seed (Figure 37.11). This is useful if you did not specify a seed to identify the seed value that reproduces the current analysis. The “Bayes Information” table provides basic information about the Monte Carlo sampling scheme. The sampling method uses a data augmentation scheme to impute component membership and then the Gamerman (1997) algorithm to sample the componentspecific parameters. The 2,000 burnin samples are followed by 10,000 Monte Carlo samples without thinning.
Figure 37.11: Model, Bayes, and Prior Information in the ZIP Model
Model Information  

Data Set  WORK.CATCH 
Response Variable  count 
Type of Model  Zeroinflated Poisson 
Components  2 
Estimation Method  Markov Chain Monte Carlo 
Random Number Seed  12345 
Bayes Information  

Sampling Algorithm  Gamerman 
Data Augmentation  Latent Variable 
Initial Values of Chain  ML Estimates 
BurnIn Size  2000 
MC Sample Size  10000 
MC Thinning  1 
Parameters in Sampling  4 
Mean Function Parameters  3 
Scale Parameters  0 
Mixing Prob Parameters  1 
Number of Threads  2 
Prior Distributions  

Component  Effect  gender  Distribution  Mean  Variance  Initial Value 
1  Intercept  Normal(0, 1000)  0  1000.00  3.5215  
1  age*gender  F  Normal(0, 1000)  0  1000.00  0.1216 
1  age*gender  M  Normal(0, 1000)  0  1000.00  0.1056 
1  Probability  Dirichlet(1, 1)  0.5000  0.08333  0.6972 
The “Prior Distributions” table identifies the prior distributions, their parameters for the sampled quantities, and their initial values. The prior distribution of parameters associated with model effects is a normal distribution with mean 0 and variance 1,000. The prior distribution for the mixing probability is a Dirichlet(1,1), which is identical to a uniform distribution (Figure 37.11). Since the second mixture component is a degeneracy at zero with no associated parameters, it does not appear in the “Prior Distributions” table in Figure 37.11.
Figure 37.12 displays descriptive statistics about the 10,000 posterior samples. Recall from Figure 37.10 that the maximum likelihood estimates were –3.5215, 0.1216, 0.1056, and 0.6972, respectively. With this choice of prior, the means of the posterior samples are generally close to the MLEs in this example. The “Posterior Intervals” table displays 95% intervals of equaltail probability and 95% intervals of highest posterior density (HPD) intervals.
Figure 37.12: Posterior Summaries and Intervals in the ZIP Model
Posterior Summaries  

Component  Effect  gender  N  Mean  Standard Deviation 
Percentiles  
25%  50%  75%  
1  Intercept  10000  3.5524  0.6509  3.9922  3.5359  3.0875  
1  age*gender  F  10000  0.1220  0.0136  0.1124  0.1218  0.1314 
1  age*gender  M  10000  0.1058  0.0140  0.0961  0.1055  0.1153 
1  Probability  10000  0.6938  0.0945  0.6293  0.6978  0.7605 
Posterior Intervals  

Component  Effect  gender  Alpha  EqualTail Interval  HPD Interval  
1  Intercept  0.050  4.8693  2.3222  4.8927  2.3464  
1  age*gender  F  0.050  0.0960  0.1494  0.0961  0.1494 
1  age*gender  M  0.050  0.0792  0.1339  0.0796  0.1341 
1  Probability  0.050  0.5041  0.8688  0.5025  0.8666 
You can generate trace plots for the posterior parameter estimates by enabling ODS Graphics:
ods graphics on; ods select TADPanel; proc fmm data=catch seed=12345; class gender; model count = gender*age / dist=Poisson; model + / dist=constant; performance cpucount=2; bayes; run; ods graphics off;
A separate trace panel is produced for each sampled parameter, and the panels for the genderspecific slopes are shown in Figure 37.13. There is good mixing in the chains: the modest autocorrelation that diminishes after about 10 successive samples. By default, the FMM procedure transfers the credible intervals for each parameter from the “Posterior Intervals” table to the trace plot and the density plot in the trace panel.