The TRANSREG Procedure
- Overview
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Getting Started
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Syntax
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Details
Model Statement Usage Box-Cox Transformations Using Splines and Knots Scoring Spline Variables Linear and Nonlinear Regression Functions Simultaneously Fitting Two Regression Functions Penalized B-Splines Smoothing Splines Smoothing Splines Changes and Enhancements Iteration History Changes and Enhancements ANOVA Codings Missing Values Missing Values, UNTIE, and Hypothesis Tests Controlling the Number of Iterations Using the REITERATE Algorithm Option Avoiding Constant Transformations Constant Variables Character OPSCORE Variables Convergence and Degeneracies Implicit and Explicit Intercepts Passive Observations Point Models Redundancy Analysis Optimal Scaling OPSCORE, MONOTONE, UNTIE, and LINEAR Transformations SPLINE and MSPLINE Transformations Specifying the Number of Knots SPLINE, BSPLINE, and PSPLINE Comparisons Hypothesis Tests Output Data Set OUTTEST= Output Data Set Computational Resources Unbalanced ANOVA without CLASS Variables Hypothesis Tests for Simple Univariate Models Hypothesis Tests with Monotonicity Constraints Hypothesis Tests with Dependent Variable Transformations Hypothesis Tests with One-Way ANOVA Using the DESIGN Output Option Discrete Choice Experiments: DESIGN, NORESTORE, NOZERO Centering Displayed Output ODS Table Names ODS Graphics
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Examples
- References
| Main-Effects ANOVA |
This example shows how to use PROC TRANSREG to code and fit a main-effects ANOVA model. PROC TRANSREG has very extensive and versatile options for coding or creating so-called dummy variables. PROC TRANSREG is commonly used to code classification variables before they are used for analysis in other procedures. See the sections Using the DESIGN Output Option and Discrete Choice Experiments: DESIGN, NORESTORE, NOZERO. In this example, the input data set contains the dependent variables y, factors x1 and x2, and 12 observations. PROC TRANSREG can be useful for coding even before running procedures with a CLASS statement because of its detailed options that enable you to control how the coded variable names and labels are constructed. The following statements perform a main-effects ANOVA and display the results in Figure 93.12 and Figure 93.13:
title 'Introductory Main-Effects ANOVA Example'; data a; input y x1 $ x2 $; datalines; 8 a a 7 a a 4 a b 3 a b 5 b a 4 b a 2 b b 1 b b 8 c a 7 c a 5 c b 2 c b ;
* Fit a main-effects ANOVA model with 1, 0, -1 coding; proc transreg ss2; model identity(y) = class(x1 x2 / effects); output coefficients replace; run; * Display TRANSREG output data set; proc print label; format intercept -- x2a 5.2; run;
The SS2 a-option requests results based on Type II sums of squares. The simple ANOVA model is fit by designating y as an IDENTITY variable, which specifies no transformation. The independent variables are specified with a CLASS expansion, which replaces them with coded variables. There are 
coded variables created by the CLASS specification, since the two CLASS variables have 3 and 2 different values or levels. In this case, the EFFECTS t-option is specified. This option requests an effects coding (displayed in Figure 93.13), which is also called a deviations from means or 0, 1, –1 coding. The OUTPUT statement requests an output data set with the data and coded variables. The COEFFICIENTS output option, or o-option, adds the parameter estimates and marginal means to the data set. The REPLACE o-option specifies that the transformed variables should replace the original variables in the output data set. The output data set variable names are the same as the original variable name. In an example like this, there are no nonlinear transformations; the transformed variables are the same as the original variables. The REPLACE o-option is used to eliminate unnecessary and redundant transformed variables from the output data set. The results of the PROC TRANSREG step are shown in Figure 93.12.
| Introductory Main-Effects ANOVA Example |
| Class Level Information | ||
|---|---|---|
| Class | Levels | Values |
| x1 | 3 | a b c |
| x2 | 2 | a b |
| Number of Observations Read | 12 |
|---|---|
| Number of Observations Used | 12 |
| Univariate ANOVA Table Based on the Usual Degrees of Freedom | |||||
|---|---|---|---|---|---|
| Source | DF | Sum of Squares | Mean Square | F Value | Pr > F |
| Model | 3 | 57.00000 | 19.00000 | 19.83 | 0.0005 |
| Error | 8 | 7.66667 | 0.95833 | ||
| Corrected Total | 11 | 64.66667 | |||
| Root MSE | 0.97895 | R-Square | 0.8814 |
|---|---|---|---|
| Dependent Mean | 4.66667 | Adj R-Sq | 0.8370 |
| Coeff Var | 20.97739 |
| Univariate Regression Table Based on the Usual Degrees of Freedom | |||||||
|---|---|---|---|---|---|---|---|
| Variable | DF | Coefficient | Type II Sum of Squares |
Mean Square | F Value | Pr > F | Label |
| Intercept | 1 | 4.6666667 | 261.333 | 261.333 | 272.70 | <.0001 | Intercept |
| Class.x1a | 1 | 0.8333333 | 4.167 | 4.167 | 4.35 | 0.0705 | x1 a |
| Class.x1b | 1 | -1.6666667 | 16.667 | 16.667 | 17.39 | 0.0031 | x1 b |
| Class.x2a | 1 | 1.8333333 | 40.333 | 40.333 | 42.09 | 0.0002 | x2 a |
Figure 93.12 shows the ANOVA results, fit statistics, and regression tables. The output data set, with the coded design, parameter estimates and means, is shown in Figure 93.13. For more information about PROC TRANSREG for ANOVA and other codings, see the section ANOVA Codings.
| Introductory Main-Effects ANOVA Example |
| Obs | _TYPE_ | _NAME_ | y | Intercept | x1 a | x1 b | x2 a | x1 | x2 |
|---|---|---|---|---|---|---|---|---|---|
| 1 | SCORE | ROW1 | 8 | 1.00 | 1.00 | 0.00 | 1.00 | a | a |
| 2 | SCORE | ROW2 | 7 | 1.00 | 1.00 | 0.00 | 1.00 | a | a |
| 3 | SCORE | ROW3 | 4 | 1.00 | 1.00 | 0.00 | -1.00 | a | b |
| 4 | SCORE | ROW4 | 3 | 1.00 | 1.00 | 0.00 | -1.00 | a | b |
| 5 | SCORE | ROW5 | 5 | 1.00 | 0.00 | 1.00 | 1.00 | b | a |
| 6 | SCORE | ROW6 | 4 | 1.00 | 0.00 | 1.00 | 1.00 | b | a |
| 7 | SCORE | ROW7 | 2 | 1.00 | 0.00 | 1.00 | -1.00 | b | b |
| 8 | SCORE | ROW8 | 1 | 1.00 | 0.00 | 1.00 | -1.00 | b | b |
| 9 | SCORE | ROW9 | 8 | 1.00 | -1.00 | -1.00 | 1.00 | c | a |
| 10 | SCORE | ROW10 | 7 | 1.00 | -1.00 | -1.00 | 1.00 | c | a |
| 11 | SCORE | ROW11 | 5 | 1.00 | -1.00 | -1.00 | -1.00 | c | b |
| 12 | SCORE | ROW12 | 2 | 1.00 | -1.00 | -1.00 | -1.00 | c | b |
| 13 | M COEFFI | y | . | 4.67 | 0.83 | -1.67 | 1.83 | ||
| 14 | MEAN | y | . | . | 5.50 | 3.00 | 6.50 |
The output data set has three kinds of observations, identified by values of _TYPE_ as follows:
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When _TYPE_=’SCORE’, the observation contains the following information about the dependent and independent variables:
y is the original dependent variable.
x1 and x2 are the independent classification variables, and the Intercept through x2 a columns contain the main-effects design matrix that PROC TRANSREG creates. The variable names are Intercept, x1a, x1b, and x2a. Their labels are shown in the listing.
When _TYPE_=’M COEFFI’, the observation contains coefficients of the final linear model (parameter estimates).
When _TYPE_=’MEAN’, the observation contains the marginal means.
The observations with _TYPE_=’SCORE’ form the score or data partition of the output data set, and the observations with _TYPE_=’M COEFFI’ and _TYPE_=’MEAN’ form the output statistics partition of the output data set.