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help for ^soreg^                                                   (Mark Lunt)
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Stereotype ordered regression 
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^soreg^ depvar varlist [^if^ exp] [^in^ range] [weight] [^,m^axdim(^#^) 
      ^c^onstraints^(^clist^)^ ^p^redict^(^newvar^)^ ]

^fweight^s are allowed; see help ^weights^.

Description
-----------

^soreg^ implements the stereotype ordinal regression model proposed by
Anderson. Constraints may be defined to perform constrained estimation.


Options
-------

^maxdim(^#^)^ defines the dimension of the stereotype model to be
    fitted. If maxdim is not specified, a one-dimensional model is
    fitted. 

^constraints(^clist^)^ specifies the linear constraints to be applied
    during estimation.  The default is to perform unconstrained
    estimation.  Constraints are defined with the constraint command.
    constraints(1) specifies that the model is to be constrained
    according to constraint 1; constraints(1-4) specifies constraints
    1 through 4; constraints(1-4,8) specifies 1 through 4 and 8.  It
    is not considered an error to specify nonexistent constraints as
    long as some of the constraints exist.  Thus, constraint(1-999)
    would specify that all defined constraints be applied.

    Stereotype regression always requires constraints in order to
    identify the model. For models in 2 dimensions or less, there are
    default constraints defined (as recommended by Anderson(1984), so
    no additional constraints need to be given. However, if ^any^
    constraints are defined, the default constraints are no longer
    applied. Therefore, you wish to add to the default constraints,
    rather than replace them, you must explicitly redefine them. 

^predict^ gives the basename for a new variable to contain the
    predicted probabilities that a given observation is in a given
    outcome category. If there are k categories in the dependent
    variables, k new variables called ^newvar1^ - ^newvark^ will be
    created, containing the probability for each subject that they are
    in each category.


Remarks
-------

Stereotype ordinal regression is an alternative to the proportional
odds ordinal regression implemented by ologit. Given an ordinal
dependent varible, and one or more predictor variables, it defines a
linear combination of the predictor variable(s) (or more than one, if
^maxdim^ is given a value greater than 1), that best discriminates
between the different categories of the dependent variable. The
coeffients of the ith variable in the jth linear combination is
labelled ^betaij^. The separation of the categories is defined by a
set of `phi' variables.


Author
------

	Mark Lunt
	ARC Epidemiology Unit
	University of Manchester
	Stopford Building
	Oxford Road
	Manchester

	Mark.Lunt@@man.ac.uk


References
----------

Anderson, J.A. (1984). "Regression and Ordered Categorical Variables."
Journal of the Royal Statistical Society, Series B 46: 1-30.

