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
Missing Values
PROC TRANSREG can estimate missing values, with or without category or monotonicity constraints, so that the regression model fit is optimized. Several approaches to missing data handling are provided. All observations with missing values in IDENTITY, CLASS, POINT, EPOINT, QPOINT, SMOOTH, PBSPLINE, PSPLINE, and BSPLINE variables are excluded from the analysis. When METHOD=UNIVARIATE (specified in the PROC TRANSREG or MODEL statement), observations with missing values in any of the independent variables are excluded from the analysis. When you specify the NOMISS a-option, observations with missing values in the other analysis variables are excluded. Otherwise, missing data are estimated, and the variable means are the initial estimates.
You can specify the LINEAR, OPSCORE, MONOTONE, UNTIE, SPLINE, MSPLINE, SSPLINE, LOG, LOGIT, POWER, ARSIN, BOXCOX, RANK, and EXP transformations in any combination with nonmissing values, ordinary missing values, and special missing values, as long as the nonmissing values in each variable have positive variance. No category or order restrictions are placed on the estimates of ordinary missing values. You can force missing value estimates within a variable to be identical by using special missing values (see “DATA Step Processing” in SAS Language Reference: Concepts. You can specify up to 27 categories of missing values, in which within-category estimates must be the same, by coding the missing values with ._ and .A through .Z.
You can also specify an ordering of some missing value estimates. You can use the MONOTONE= a-option in the PROC TRANSREG or MODEL statement to indicate a range of special missing values (a subset of the list from .A to .Z) with estimates that must be weakly ordered within each variable in which they appear. For example, if MONOTONE=AI, the nine classes, .A, .B, …, .I, are monotonically scored and optimally scaled just as MONOTONE transformation values are scored. In this case, category but not order restrictions are placed on the missing values ._ and .J through .Z. You can also use the UNTIE= a-option (in the PROC TRANSREG or MODEL statement) to indicate a range of special missing values with estimates that must be weakly ordered within each variable in which they appear but can be untied.
The missing value estimation facilities enable you to have partitioned or mixed-type variables. For example, a variable can be considered part nominal and part ordinal. Nominal classes of otherwise ordinal variables are coded with special missing values. This feature can be useful with survey research. The class “unfamiliar with the product” in the variable “Rate your preference for ’Brand X’ on a 1 to 9 scale, or if you are unfamiliar with the product, check ’unfamiliar with the product’” is an example. You can code “unfamiliar with the product” as a special missing value, such as .A. The 1s to 9s can be monotonically transformed, while no monotonic restrictions are placed on the quantification of the “unfamiliar with the product” class.
A variable specified for a LINEAR transformation, with special missing values and ordered categorical missing values, can be part interval, part ordinal, and part nominal. A variable specified for a MONOTONE transformation can have two independent ordinal parts. A variable specified for an UNTIE transformation can have an ordered categorical part and an ordered part without category restrictions. Many other mixes are possible.