sample_3wages_comuna.raw 	! location of dataset
500					! iterations to save 
100					! burnin 
30					! step 
37089		   	            ! number of individuals 
114					! number of variables 
3					! number of probits 
11					! number of linear equations 
	! FACTORS
F 7				! Problems, Where 
2					! Number of factors
	!  Factors
F					! Restrict factor to be normal? 
2					! Mixture elements for factor 
T					! Make the mean of the factor zero? 
F					! Make the first scale equal to 1 
F					! Scale mixture only? (restrict all means to zero) 
F					! Mean mixture only? (restrict all scales to 1) 
fac_prod.out				! path of output file for mixture parameters (mu,tau,p) 
F					! Restrict factor to be normal? 
2					! Mixture elements for factor 
T					! Make the mean of the factor zero? 
F					! Make the first scale equal to 1 
F					! Scale mixture only? (restrict all means to zero) 
F					! Mean mixture only? (restrict all scales to 1) 
fac_test.out				! path of output file for mixture parameters (mu,tau,p) 
	! PROBIT (Train Year 1)
10 					!location of outcome
T 11					! Indicator for Missings, Location
F 					! Is the error term a mixture
6					! number of X's
5 6 8  67 100  114 ! location of X's in dataset
F 5					! Drop variables predict perfectly? Location
F					! Normalizations In loadings
training_1.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! PROBIT (Train Year 2 if yes)
12 					!location of outcome
T 13				! Indicator for Missings, Location
F 					! Is the error term a mixture
6					! number of X's
5 6 8  68 101 114  ! location of X's in dataset
F 5					! Drop variables predict perfectly? Location
F					! Normalizations In loadings
training_11.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! PROBIT (Train Year 2 if no first)
14 					!location of outcome
T 15					! Indicator for Missings, Location
F 					! Is the error term a mixture
6					! number of X's
5 6 8  68 101 114  ! location of X's in dataset
F 5					! Drop variables predict perfectly? Location
F					! Normalizations In loadings
training_10.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: 11 )
16 					!location of outcome
T 17					! Indicator for Missings, Location
F 					! Is the error term a mixture
10					! number of X's
5 6 8 9 91 92 93 94 95 114 ! location of X's in dataset
F					! Normalizations In loadings
salary_11.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: 10 )
18 					!location of outcome
T 19					! Indicator for Missings, Location
F 					! Is the error term a mixture
10					! number of X's
5 6 8 9 91 92 93 94 95 114 ! location of X's in dataset
F					! Normalizations In loadings
salary_10.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: 01)
20 					!location of outcome
T 21					! Indicator for Missings, Location
F 					! Is the error term a mixture
10					! number of X's
5 6 8 9 91 92 93 94 95 114 ! location of X's in dataset
F					! Normalizations In loadings
salary_01.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: 00 )
22 					!location of outcome
T 23				! Indicator for Missings, Location
F 					! Is the error term a mixture
10					! number of X's
5 6 8 9 91 92 93 94 95 114 ! location of X's in dataset
F					! Normalizations In loadings
salary_00.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: 1 )
24 					!location of outcome
T 25					! Indicator for Missings, Location
F 					! Is the error term a mixture
10					! number of X's
5 6 8 9 91 92 93 94 95 114 ! location of X's in dataset
F					! Normalizations In loadings
salary_1.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: 0 )
26 					!location of outcome
T 27				! Indicator for Missings, Location
F 					! Is the error term a mixture
10					! number of X's
5 6 8 9 91 92 93 94 95 114 ! location of X's in dataset
F					! Normalizations In loadings
salary_0.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: X )
28 					!location of outcome
T 29				! Indicator for Missings, Location
F 					! Is the error term a mixture
8					! number of X's
5 6 8 91 92 93 94 95  ! location of X's in dataset
T					! Normalizations In loadings
1.0 0.0 !
1.0 0.0 ! 
salary_X1.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (Salary: X )
108 					!location of outcome
T 29				! Indicator for Missings, Location
F 					! Is the error term a mixture
8					! number of X's
5 6 8 91 92 93 94 95  ! location of X's in dataset
T					! Normalizations In loadings
0.0 0.0 !
0.0 0.0 ! 
salary_X2.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (PSU_mate)
2 					!location of outcome
T 11					! Indicator for Missings, Location
F 					! Is the error term a mixture
8					! number of X's
5 6 7 85 86 87 88 89 ! location of X's in dataset
T					! Normalizations In loadings
1.0 1.0 !
0.0 1.0 ! 
mate.out				! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (PSU_verbal)
3 					!location of outcome
T 11					! Indicator for Missings, Location
F 					! Is the error term a mixture
8					! number of X's
5 6 7 85 86 87 88 89 ! location of X's in dataset
T					! Normalizations In loadings
1.0 0.0 !
0.0 0.0 ! 
verbal.out	! output path
F 5000				! Use previous run as initial value, what iteration
	! LINEAR MODEL (GPA)
4 					!location of outcome
T 11					! Indicator for Missings, Location
F 					! Is the error term a mixture
8					! number of X's
5 6 7 85 86 87 88 89 ! location of X's in dataset
T					! Normalizations In loadings
1.0 0.0 !
0.0 0.0 ! 
nem.out	! output path
F 5000				! Use previous run as initial value, what iteration
 	! Precision 
0.1					! prior precision for all loadings (i.e. P(a)~N(0,thisnumber))
	! Precision for slopes 
0.1					! prior precision for all loadings (i.e. P(b)~N(0,thisnumber))