The TRANSREG Procedure

ODS Graphics

Subsections:

The PLOTS(INTERPOLATE) Option

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, by specifying 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 101.8, along with the required statements and options.

Table 101.8: Graphs Produced by PROC TRANSREG

ODS Graph Name	Plot Description	Statement & Option
BoxCoxFPlot	Box-Cox $F = t^2$	MODEL & PROC, BOXCOX transform & PLOTS(UNPACK)
BoxCoxLogLikePlot	Box-Cox Log Likelihood	MODEL & PROC, BOXCOX transform & PLOTS(UNPACK)
BoxCoxPlot	Box-Cox t or $F = t^2$ & Log Likelihood	MODEL, BOXCOX transform
BoxCoxtPlot	Box-Cox t	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 101.77 and Figure 101.78:

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 101.77. The transformation and fit functions are not at all smooth. The results with interpolation are shown in Figure 101.78. The transformation and fit functions are smooth in Figure 101.78, because there are intermediate points to plot.

Figure 101.77: 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

Figure 101.78: 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