The MODEL statement defines elements of the mixture model, such as the model effects, the distribution, and the link function. At least one MODEL statement is required. You can specify more than one MODEL statement. Each MODEL statement identifies one or more components of a mixture. For example, if components differ in their distributions, link functions, or regressor variables, then you can use separate MODEL statements to define the components. If the finite mixture model is homogeneous—in the sense that all components share the same regressors, distribution, and link function—then you can specify the mixture model with a single MODEL statement by using the K= option.
An intercept is included in each model by default. It can be removed with the NOINT option.
The dependent variable can be specified by using either the response syntax or the events/trials syntax. The events/trials syntax is specific to models for binomialtype data. A binomial(n, ) variable is the sum of n independent Bernoulli trials with event probability . Each Bernoulli trial results in either an event or a nonevent (with probability ). The value of the second variable, trials, gives the number n of Bernoulli trials. The value of the first variable, events, is the number of events out of n. The values of both events and (trials–events) must be nonnegative, and the value of trials must be positive. Other distributions that allow the events/trials syntax are the betabinomial distribution and the binomial cluster model.
If the events/trials syntax is used, the FMM procedure defaults to the binomial distribution. If you use the response syntax, the procedure defaults to the normal distribution unless the response variable is a character variable or listed in the CLASS statement.
You use a similar syntax to fit multinomial models or multinomial cluster models, except that for these distributions you specify multiple dependent variables, one for each value of the multinomial response. For these models, you can specify either multiple response variables or multiple event variables. If you use multiple response or multiple event variables, the FMM procedure defaults to the multinomial distribution. If you use the multiple response syntax, the FMM procedure treats the total of these responses as fixed.
The FMM procedure supports a continuationstyle syntax in MODEL statements. Because a mixture has only one set of response variables, it is sufficient to specify the response variable in one MODEL statement. Other MODEL statements can use the continuation symbol "+" before the specification of effects. For example, the following statements fit a threecomponent binomial mixture model:
class A; model y/n = x / k=2; model + A;
The first MODEL statement uses the "=" sign to separate response from effect information and specifies the response variable
by using the events/trials syntax. This determines the distribution as binomial. This MODEL statement adds two components to the mixture models with
different intercepts and regression slopes. The second MODEL statement adds another component to the mixture where the mean
is a function of the classification main effect for variable A
. The response is also binomial; it is a continuation from the previous MODEL statement.
There are two sets of options in the MODEL statement. The responseoptions determine how the FMM procedure models probabilities for binary data. The modeloptions control other aspects of model formation and inference. Table 39.4 summarizes the responseoptions and modeloptions available in the MODEL statement. These are subsequently discussed in detail in alphabetical order by option category.
Table 39.4: Summary of MODEL Statement Options
Option 
Description 

Response Variable Options 

Reverses the order of response categories 

Specifies the event category in binary models 

Specifies the sort order for the response variable 

Specifies the reference category in binary models 

Model Building 

Specifies the response distribution 

Specifies the link function 

Specifies the number of mixture components 

Specifies the maximum number of mixture components 

Specifies the minimum number of mixture components 

Requests that the starting values for each analysis be determined separately instead of sequentially 

Excludes fixedeffect intercept from model 

Specifies the offset variable for linear predictor 

Statistical Computations and Output 

Determines the confidence level () 

Displays confidence limits for fixedeffects parameter estimates 

Imposes simple equality constraints on parameters in this model 

Identifies the model 

Provides starting values for the parameters in this model 