Previous Page | Next Page

The TRANSREG Procedure

ODS Graphics

To request graphics with PROC TRANSREG, you must first enable ODS Graphics by specifying the ODS GRAPHICS ON statement. See Chapter 21, Statistical Graphics Using ODS, for more information. Some graphs are produced by default; other graphs are produced by using statements and options. You can reference every graph produced through ODS Graphics with a name. The names of the graphs that PROC TRANSREG generates are listed in Table 90.8, along with the required statements and options.

Table 90.8 ODS Graphics Produced by PROC TRANSREG

ODS Graph Name

Plot Description

Statement & Option

BoxCoxFPlot

Box-Cox

MODEL & PROC, BOXCOX transform & PLOTS(UNPACK)

BoxCoxLogLikePlot

Box-Cox Log
Likelihood

MODEL & PROC, BOXCOX transform & PLOTS(UNPACK)

BoxCoxPlot

Box-Cox or &
Log Likelihood

MODEL, BOXCOX transform

BoxCoxtPlot

Box-Cox

MODEL & PROC, BOXCOX transform &
PLOTS(UNPACK)=BOXCOX(T)

FitPlot

Simple Regression and Separate Group Regressions

MODEL, a dependent variable that is not transformed, one non-CLASS independent variable, and at most one CLASS variable

ObservedByPredicted

Dependent Variable by
Predicted Values

MODEL, PLOTS=OBSERVEDBYPREDICTED

PBSPlineCritPlot

Penalized B-Spline
Criterion Plot

MODEL, PBSPLINE transform

PrefMapVecPlot

Preference Mapping
Vector Plot

MODEL & PROC, IDENTITY transform & COORDINATES

PrefMapIdealPlot

Preference Mapping
Ideal Point Plot

MODEL & PROC, POINT expansion & COORDINATES

ResidualPlot

Residuals

PROC, PLOTS=RESIDUALS

RMSEPlot

Box-Cox Root Mean
Square Error

MODEL & PROC, BOXCOX transform &
PLOTS=BOXCOX(RMSE)

ScatterPlot

Scatter Plot of Observed Data

MODEL, one non-CLASS independent variable, and at most one CLASS variable, PLOTS=SCATTER

TransformationPlot

Variable Transformations

PROC, PLOTS=TRANSFORMATION

The PLOTS(INTERPOLATE) Option

This section illustrates one use of the PLOTS(INTERPOLATE) option for use with ODS Graphics. The data set has two groups of observations, c = 1 and c = 2. Each group is sparse, having only five observations, so the plots of the transformations and fit functions are not smooth. A second DATA step adds additional observations to the data set, over the range of x, with y missing. These observations do not contribute to the analysis, but they are used in computations of transformed and predicted values. The resulting plots are much smoother in the latter case than in the former. The other results of the analysis are the same. The following statements produce Figure 90.78 and Figure 90.79:

   title 'Smoother Interpolation with PLOTS(INTERPOLATE)';
   
   data a;
      input c y x;
      output;
      datalines;
   1 1 1
   1 2 2
   1 4 3
   1 6 4
   1 7 5
   2 3 1
   2 4 2
   2 5 3
   2 4 4
   2 5 5
   ;
   ods graphics on;
   
   proc transreg data=a plots=(tran fit) ss2;
      model ide(y) = pbs(x) * class(c / zero=none);
      run;
   
   data b;
      set a end=eof;
      output;
      if eof then do;
         y = .;
         do x = 1 to 5 by 0.05;
            c = 1; output;
            c = 2; output;
            end;
         end;
      run;
   
   proc transreg data=b plots(interpolate)=(tran fit) ss2;
      model ide(y) = pbs(x) * class(c / zero=none);
      run;
   
   ods graphics off;

The results with no interpolation are shown in Figure 90.78. The transformation and fit functions are not at all smooth. The results with interpolation are shown in Figure 90.79. The transformation and fit functions are smooth in Figure 90.79, because there are intermediate points to plot.

Figure 90.78 No Interpolation
Smoother Interpolation with PLOTS(INTERPOLATE)

The TRANSREG Procedure

Univariate ANOVA Table, Penalized B-Spline Transformation
Source DF Sum of Squares Mean Square F Value Pr > F
Model 9 28.90000 3.211111 Infty <.0001
Error 12E-10 0.00000 0.000000    
Corrected Total 9 28.90000      

Root MSE 0 R-Square 1.0000
Dependent Mean 4.10000 Adj R-Sq 1.0000
Coeff Var 0    

Penalized B-Spline Transformation
Variable DF Coefficient Lambda AICC Label
Pbspline(xc1) 5.0000 1.000 2.642E-7 -66.4281 x * c 1
Pbspline(xc2) 5.0000 1.000 2.516E-7 -60.6430 x * c 2

trgd7btrgd7b, continued

Figure 90.79 Interpolation with PLOTS(INTERPOLATE)
Smoother Interpolation with PLOTS(INTERPOLATE)

The TRANSREG Procedure

Univariate ANOVA Table, Penalized B-Spline Transformation
Source DF Sum of Squares Mean Square F Value Pr > F
Model 9 28.90000 3.211111 Infty <.0001
Error 12E-10 0.00000 0.000000    
Corrected Total 9 28.90000      

Root MSE 0 R-Square 1.0000
Dependent Mean 4.10000 Adj R-Sq 1.0000
Coeff Var 0    

Penalized B-Spline Transformation
Variable DF Coefficient Lambda AICC Label
Pbspline(xc1) 5.0000 1.000 2.642E-7 -66.4281 x * c 1
Pbspline(xc2) 5.0000 1.000 2.516E-7 -60.6430 x * c 2

trgd7dtrgd7d, continued

Previous Page | Next Page | Top of Page