! Comments follow exclamation points
! see: DeCarlo, L. T. (2002). A latent class extension of signal
! detection theory, with applications. Multivariate Behavioral
! Research, 37, 423-451.

TITLE: Ordinal response, multiple sclerosis data (Landis & Koch 1977);
DATA:  FILE IS C:\mplus\files\landis.txt;

! NOTE: if data are in tabular form, they must be written out as individual
! records. This can be done using statistical packages (like SAS)

VARIABLE: NAMES ARE y1 y2;
          CLASSES = class(2);
          CATEGORICAL=y1-y2;
ANALYSIS: TYPE=MIXTURE; ESTIMATOR=ML;

! See Appendix B for some comments on how the model is parameterized

MODEL: %OVERALL%
        f1 by y1@1; f2 by y2@1;

! Factors are included above to allow for non-zero means below

        [y1$1*-1] (1); [y1$2*0] (2); [y1$3*1] (3);
        [y2$1*-1] (4); [y2$2*0] (5); [y2$3*1] (6); 

! The numbers in parentheses restrict the thresholds to be equal
! across the latent classes

        %class#1%
        [f1@0 f2@0];
        %class#2%
        [f1*1 f2*1];

!  The above allows the means to be non-zero in one latent class.
!  This gives the detection parameters

OUTPUT: sampstat tech1;