ODS Graphics 
Statistical procedures use ODS Graphics to create graphs as part of their output. ODS Graphics is described in detail in Chapter 21, Statistical Graphics Using ODS.
Before you create graphs, ODS Graphics must be enabled (for example, with the ODS GRAPHICS ON statement). For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21, Statistical Graphics Using ODS.
The overall appearance of graphs is controlled by ODS styles. Styles and other aspects of using ODS Graphics are discussed in the section A Primer on ODS Statistical Graphics in Chapter 21, Statistical Graphics Using ODS.
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 93.8, along with the required statements and options.
ODS Graph Name 
Plot Description 
Statement & Option 

BoxCoxFPlot 
BoxCox 
MODEL & PROC, BOXCOX transform & PLOTS(UNPACK) 
BoxCoxLogLikePlot 
BoxCox Log 
MODEL & PROC, BOXCOX transform & PLOTS(UNPACK) 
BoxCox t or & 
MODEL, BOXCOX transform 

BoxCoxtPlot 
BoxCox t 
MODEL & PROC, BOXCOX transform & 
Simple Regression and Separate Group Regressions 
MODEL, a dependent variable that is not transformed, one nonCLASS independent variable, and at most one CLASS variable 

Dependent Variable by 
MODEL, PLOTS=OBSERVEDBYPREDICTED 

Penalized BSpline 
MODEL, PBSPLINE transform 

Preference Mapping 
MODEL & PROC, IDENTITY transform & COORDINATES 

Preference Mapping 
MODEL & PROC, POINT expansion & COORDINATES 

Residuals 
PROC, PLOTS=RESIDUALS 

RMSEPlot 
BoxCox Root Mean 

ScatterPlot 
Scatter Plot of Observed Data 
MODEL, one nonCLASS independent variable, and at most one CLASS variable, PLOTS=SCATTER 
Variable Transformations 
PROC, PLOTS=TRANSFORMATION 
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 93.77 and Figure 93.78:
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;
The results with no interpolation are shown in Figure 93.77. The transformation and fit functions are not at all smooth. The results with interpolation are shown in Figure 93.78. The transformation and fit functions are smooth in Figure 93.78, because there are intermediate points to plot.
Smoother Interpolation with PLOTS(INTERPOLATE) 
Univariate ANOVA Table, Penalized BSpline Transformation  

Source  DF  Sum of Squares  Mean Square  F Value  Pr > F 
Model  9  28.90000  3.211111  Infty  <.0001 
Error  12E10  0.00000  0.000000  
Corrected Total  9  28.90000 
Root MSE  0  RSquare  1.0000 

Dependent Mean  4.10000  Adj RSq  1.0000 
Coeff Var  0 
Penalized BSpline Transformation  

Variable  DF  Coefficient  Lambda  AICC  Label 
Pbspline(xc1)  5.0000  1.000  2.642E7  66.4281  x * c 1 
Pbspline(xc2)  5.0000  1.000  2.516E7  60.6430  x * c 2 
Smoother Interpolation with PLOTS(INTERPOLATE) 
Univariate ANOVA Table, Penalized BSpline Transformation  

Source  DF  Sum of Squares  Mean Square  F Value  Pr > F 
Model  9  28.90000  3.211111  Infty  <.0001 
Error  12E10  0.00000  0.000000  
Corrected Total  9  28.90000 
Root MSE  0  RSquare  1.0000 

Dependent Mean  4.10000  Adj RSq  1.0000 
Coeff Var  0 
Penalized BSpline Transformation  

Variable  DF  Coefficient  Lambda  AICC  Label 
Pbspline(xc1)  5.0000  1.000  2.642E7  66.4281  x * c 1 
Pbspline(xc2)  5.0000  1.000  2.516E7  60.6430  x * c 2 