The TRANSREG Procedure
- Overview
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Getting Started
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Syntax
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DetailsModel Statement UsageBox-Cox TransformationsUsing Splines and KnotsScoring Spline VariablesLinear and Nonlinear Regression FunctionsSimultaneously Fitting Two Regression FunctionsPenalized B-SplinesSmoothing SplinesSmoothing Splines Changes and EnhancementsIteration History Changes and EnhancementsANOVA CodingsMissing ValuesMissing Values, UNTIE, and Hypothesis TestsControlling the Number of IterationsUsing the REITERATE Algorithm OptionAvoiding Constant TransformationsConstant VariablesCharacter OPSCORE VariablesConvergence and DegeneraciesImplicit and Explicit InterceptsPassive ObservationsPoint ModelsRedundancy AnalysisOptimal ScalingOPSCORE, MONOTONE, UNTIE, and LINEAR TransformationsSPLINE and MSPLINE TransformationsSpecifying the Number of KnotsSPLINE, BSPLINE, and PSPLINE ComparisonsHypothesis TestsOutput Data SetOUTTEST= Output Data SetComputational ResourcesUnbalanced ANOVA without CLASS VariablesHypothesis Tests for Simple Univariate ModelsHypothesis Tests with Monotonicity ConstraintsHypothesis Tests with Dependent Variable TransformationsHypothesis Tests with One-Way ANOVAUsing the DESIGN Output OptionDiscrete Choice Experiments: DESIGN, NORESTORE, NOZEROCenteringDisplayed OutputODS Table NamesODS Graphics
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Examples
- References
Convergence and Degeneracies
When you specify the SSPLINE transformation, divergence is normal. The rest of this section assumes that you did not specify SSPLINE. For all the methods available in PROC TRANSREG, the algorithms are convergent, in terms of both the criterion being optimized and the parameters being estimated. The value of the criterion being maximized (squared multiple correlation, average squared multiple correlation, or average squared canonical correlation) can, theoretically, never decrease from one iteration to the next. The values of the parameters being solved for (the scores and weights of the transformed variables) become stable after sufficient iteration.
In practice, the criterion being maximized can decrease with overiteration. When the statistic has very nearly reached its maximum, further iterations might report a decrease in the criterion in the last few decimal places. This is a normal result of very small amounts of rounding error. By default, iteration terminates when this occurs because, by default, CCONVERGE=0.0. Specifying CCONVERGE=–1, an impossible change, turns off this check for convergence.
Even though the algorithms are convergent, they might not converge to a global optimum. Also, under extreme circumstances, the solution might degenerate. Because two points always form a straight line, the algorithms sometimes try to reach this degenerate optimum. This sometimes occurs when one observation is an ordinal outlier (when one observation has the extreme rank on all variables). The algorithm can reach an optimal solution that ties all other categories producing two points. Similar results can occur when there are many missing values. More generally, whenever there are very few constraints on the scoring of one or more points, degeneracies can be a problem. In a well-behaved analysis, the maximum data change, average data change, and criterion change all decrease at a rapid rate with each iteration. When the rate of change increases for several iterations, the solution might be degenerating.