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Example
34
Mixture Modeling with Training Data
Introduction
Mixture modeling is appropriate when you have a model that is incorrect for an entire
population, but where the population can be divided into subgroups in such a way that
the model is correct in each subgroup.
Mixture modeling is discussed in the context of structural equation modeling by
Arminger, Stein, and Wittenberg (1999), Hoshino (2001), Lee (2007, Chapter 11),
Loken (2004), Vermunt and Magidson (2005), and Zhu and Lee (2001), among
others.
The present example demonstrates mixture modeling for the situation in which
some cases have already been assigned to groups while other cases have not. It is up
to Amos to learn from the cases that are already classified and to classify the others.
We begin mixture modeling with an example in which some cases have already
been classified because setting up such an analysis is almost identical to setting up an
ordinary multiple-group analysis such as in Examples 10, 11, and 12.
It is possible to perform mixture modeling when no cases have been classified in
advance so that the program must classify every case. Example 35 demonstrates this
type of analysis.
About the Data
The data for this example were collected by Anderson (1935) and used by Fisher
(1936) to demonstrate discriminant analysis. The original data are in the file iris.sav,
of which a portion is shown here: