Interactive Analysis

The REG Procedure

Interactive Analysis

PROC REG enables you to change interactively both the model and the data used to compute the model, and to produce and highlight scatter plots. The following statements can be used interactively (without reinvoking PROC REG): ADD, DELETE, MODEL, MTEST, OUTPUT, PAINT, PLOT, PRINT, REFIT, RESTRICT, REWEIGHT, and TEST. All interactive features are disabled if there is a BY statement.

The ADD, DELETE and REWEIGHT statements can be used to modify the current MODEL. Every use of an ADD, DELETE or REWEIGHT statement causes the model label to be modified by attaching an additional number to it. This number is the cumulative total of the number of ADD, DELETE or REWEIGHT statements following the current MODEL statement.

A more detailed explanation of changing the data used to compute the model is given in the section "Reweighting Observations in an Analysis". Extra features for line printer scatter plots are discussed in the section "Line Printer Scatter Plot Features".

The following example illustrates the usefulness of the interactive features. First, the full regression model is fit to the class data (see the "Getting Started" section), and Figure 55.22 is produced.

   proc reg data=Class;
      model Weight=Age Height;  
   run;

The REG Procedure

Model: MODEL1

Dependent Variable: Weight

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	2	7215.63710	3607.81855	27.23	<.0001
Error	16	2120.09974	132.50623
Corrected Total	18	9335.73684

Root MSE	11.51114	R-Square	0.7729
Dependent Mean	100.02632	Adj R-Sq	0.7445
Coeff Var	11.50811

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > \|t\|
Intercept	1	-141.22376	33.38309	-4.23	0.0006
Age	1	1.27839	3.11010	0.41	0.6865
Height	1	3.59703	0.90546	3.97	0.0011

Figure 55.23: Interactive Analysis: Full Model

Next, the regression model is reduced by the following statements, and Figure 55.23 is produced.

   delete age;
   print;
   run;

The REG Procedure

Model: MODEL1.1

Dependent Variable: Weight

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	1	7193.24912	7193.24912	57.08	<.0001
Error	17	2142.48772	126.02869
Corrected Total	18	9335.73684

Root MSE	11.22625	R-Square	0.7705
Dependent Mean	100.02632	Adj R-Sq	0.7570
Coeff Var	11.22330

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > \|t\|
Intercept	1	-143.02692	32.27459	-4.43	0.0004
Height	1	3.89903	0.51609	7.55	<.0001

Figure 55.24: Interactive Analysis: Reduced Model

Note that the MODEL label has been changed from MODEL1 to MODEL1.1, as the original MODEL has been changed by the delete statement.

The following statements generate a scatter plot of the residuals against the predicted values from the full model. Figure 55.24 is produced, and the scatter plot shows a possible outlier.

   add age;
   plot r.*p. / cframe=ligr;
   run;

Figure 55.25: Interactive Analysis: Scatter Plot

The following statements delete the observation with the largest residual, refit the regression model, and produce a scatter plot of residuals against predicted values for the refitted model. Figure 55.25 shows the new scatter plot.

   reweight r.>20;
   plot / cframe=ligr;
   run;

Figure 55.26: Interactive Analysis: Scatter Plot for Refitted Model

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