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The TRANSREG Procedure |

Several *a-options* in the PROC TRANSREG or MODEL statement
control the number of iterations performed.
Iteration terminates when any one of the
following conditions is satisfied:

- The number of iterations equals the value of the MAXITER=
*a-option*. - The average absolute change in variable scores from one
iteration to the next is less than the value of the CONVERGE=
*a-option*. - The criterion change is less than the
value of the CCONVERGE=
*a-option*.

You can specify negative values for either convergence option if you
wish to define convergence only in terms of the other option. The
criterion change can become negative when the data have converged so
that it is numerically impossible, within machine precision, to increase
the criterion. Usually, a negative criterion change is the result of
very small amounts of rounding error since the algorithms are (usually)
convergent. However, there are other cases where a negative criterion
change is a sign of divergence, which is not necessarily an error. When
you specify an SSPLINE transformation or the REITERATE or DUMMY
*a-option*, divergence may be perfectly normal.

When there are no monotonicity constraints and there is only one
canonical variable in each set, PROC TRANSREG (with the DUMMY *a-option*) can
usually find the optimal solution in only one iteration. (There are no
monotonicity constraints when the MONOTONE, MSPLINE, or UNTIE
transformations and the UNTIE= and MONOTONE= *a-options* are not specified.
There is only one canonical variable in each set when METHOD=MORALS or
METHOD=UNIVARIATE, or when METHOD=REDUNDANCY with only one dependent
variable, or when METHOD=CANALS and NCAN=1.)

The initialization iteration is number 0. When there are no
monotonicity constraints and there is only one canonical variable in
each set, the next iteration shows no change and iteration stops.
At least two iterations (0 and 1) are performed with the DUMMY *a-option*
even if nothing changes in iteration 0. The MONOTONE, MSPLINE, and
UNTIE variables are not transformed by the dummy variable
initialization. Note that divergence with the DUMMY *a-option*,
particularly in the second iteration, is not an error. The
initialization iteration is slower and uses more memory than other
iterations. However, for many models, specifying the DUMMY *
a-option* can greatly decrease the amount
of time required to find the optimal transformations. Furthermore, by
solving for the transformations directly instead of iteratively, PROC
TRANSREG avoids certain nonoptimal solutions.

You can increase the number of iterations to ensure convergence by
increasing the value of the MAXITER= *a-option* and decreasing the value
of the CONVERGE= *a-option*.
Since the average absolute change in standardized variable scores seldom
decreases below 1E-11, you should not specify a value for the CONVERGE=
*a-option* less than 1E-8 or 1E-10. Most of the data changes
occur during the
first few iterations, but the data can still change after 50 or even 100
iterations. You can try different combinations of values for the CONVERGE=
and MAXITER= *a-options* to ensure convergence without extreme overiteration. If
the data do not converge with the default specifications, try
CONVERGE=1E-8 and MAXITER=50, or CONVERGE=1E-10 and MAXITER=200.
Note that you can specify the REITERATE *a-option* to start
iterating where the previous analysis stopped.

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