TITLE: mix1

DATA: FILE IS bart.dat;

VARIABLE: NAMES ARE u1-u4;
          USEV ARE  u1-u4;
          CATEGORICAL = u1 - u4;
          CLASSES = c(2);

ANALYSIS: TYPE=MIXTURE;
          MITERATIONS = 100;  

!	this is a latent class analysis of 4 binary indicators of a 
!	categorical latent variable with 2 classes
!	the default number of E step iterations is reduced from 100
!	to 60 because this example converges quickly even with rough
!	starting values                                                   
          
MODEL:
	%OVERALL%
  
  %C#1%
  [u1$1*2 u2$1*2 u3$1*2 u4$1*2];

  %C#2%
  [u1$1*-1 u2$1*-1 u3$1*-1 u4$1*-1];

!	the two lines above refer to the logits of the conditional  
!	probabilities of u = 1 given latent class 1 and 2, respectively. 
!	Starting  values are required for these parameters. 
!	Starting values can for example be obtained  
!	by having lower u probabilities for the first class than for the second
!	class. There is no need to provide starting values for the latent class 
!	probabilities - the default is equal probabilities. As an example of  
!	giving a starting value with a small probability for class 1 is as 
!	follows:
!	
!	[c#1*-2];	
!
!	The following shows how to set starting values in the logit scale.

!	the relationship between logits and probabilities is
!
!	probability = 1/(1+exp(-logit))
!
!	logit = elog(probability/(1-probability))	
!
!	which means that
!
!	Probability	Logit
!	0		-100 (approximately)
!	0.5		0
!	1		+100 (approximately)


OUTPUT:
	TECH8;

!	tech8 is needed to monitor the convergence of mixture modeling