PROC HPQUANTSELECT supports the ability to split classification variables when you do model selection. You use the SPLIT option in the CLASS statement to specify that the columns of the design matrix that correspond to effects that contain a split classification variable can enter or leave a model independently of the other design columns of that effect. The following statements illustrate the use of the SPLIT option:

data splitExample; length C2 $6; drop i; do i=1 to 1000; C1 = 1 + mod(i,6); if i < 250 then C2 = 'Low'; else if i < 500 then C2 = 'Medium'; else C2 = 'High'; x1 = ranuni(1); x2 = ranuni(1); y = x1+3*(C2 ='low') + 10*(C1=3) +5*(C1=5) + rannor(1); output; end; run; proc hpquantselect data=splitExample; class C1(split) C2(order=data); model y = C1 C2 x1 x2/orderselect clb; selection method=forward; run;

The "Class Levels" table in Figure 14.14 is produced by default whenever you specify a CLASS statement.

Figure 14.14: Class Levels

The SPLIT option has been specified for the classification variable `C1`

. This permits the parameters that are associated with the effect `C1`

to enter or leave the model individually. The "Parameter Estimates" table in Figure 14.15 shows that for this example the parameters that correspond to only levels 3 and 5 of `C1`

are in the selected model. Finally, note that the ORDERSELECT
option in the MODEL
statement displays the parameters in the order in which they first entered the model.

Figure 14.15: Parameter Estimates

Parameter Estimates | |||||||
---|---|---|---|---|---|---|---|

Parameter | DF | Estimate | Standard Error |
95% Confidence Limits | t Value | Pr > |t| | |

Intercept | 1 | -0.21596 | 0.09024 | -0.39304 | -0.03887 | -2.39 | 0.0169 |

C1_3 | 1 | 10.08952 | 0.09852 | 9.89619 | 10.28285 | 102.41 | <.0001 |

C1_5 | 1 | 5.04115 | 0.10835 | 4.82854 | 5.25376 | 46.53 | <.0001 |

x1 | 1 | 1.29863 | 0.14014 | 1.02363 | 1.57363 | 9.27 | <.0001 |