Getting Started Example 2 for PROC ROBUSTREG
/****************************************************************/
/* S A S S A M P L E L I B R A R Y */
/* */
/* NAME: rreggs2 */
/* TITLE: Getting Started Example 2 for PROC ROBUSTREG */
/* PRODUCT: STAT */
/* SYSTEM: ALL */
/* KEYS: Robust Regression */
/* */
/* PROCS: ROBUSTREG */
/* DATA: */
/* */
/* SUPPORT: Yonggang Yao */
/* REF: */
/* MISC: */
/****************************************************************/
data hbk;
input index $ x1 x2 x3 y @@;
datalines;
1 10.1 19.6 28.3 9.7 2 9.5 20.5 28.9 10.1
3 10.7 20.2 31.0 10.3 4 9.9 21.5 31.7 9.5
5 10.3 21.1 31.1 10.0 6 10.8 20.4 29.2 10.0
7 10.5 20.9 29.1 10.8 8 9.9 19.6 28.8 10.3
9 9.7 20.7 31.0 9.6 10 9.3 19.7 30.3 9.9
11 11.0 24.0 35.0 -0.2 12 12.0 23.0 37.0 -0.4
13 12.0 26.0 34.0 0.7 14 11.0 34.0 34.0 0.1
15 3.4 2.9 2.1 -0.4 16 3.1 2.2 0.3 0.6
17 0.0 1.6 0.2 -0.2 18 2.3 1.6 2.0 0.0
19 0.8 2.9 1.6 0.1 20 3.1 3.4 2.2 0.4
21 2.6 2.2 1.9 0.9 22 0.4 3.2 1.9 0.3
23 2.0 2.3 0.8 -0.8 24 1.3 2.3 0.5 0.7
25 1.0 0.0 0.4 -0.3 26 0.9 3.3 2.5 -0.8
27 3.3 2.5 2.9 -0.7 28 1.8 0.8 2.0 0.3
29 1.2 0.9 0.8 0.3 30 1.2 0.7 3.4 -0.3
31 3.1 1.4 1.0 0.0 32 0.5 2.4 0.3 -0.4
33 1.5 3.1 1.5 -0.6 34 0.4 0.0 0.7 -0.7
35 3.1 2.4 3.0 0.3 36 1.1 2.2 2.7 -1.0
37 0.1 3.0 2.6 -0.6 38 1.5 1.2 0.2 0.9
39 2.1 0.0 1.2 -0.7 40 0.5 2.0 1.2 -0.5
41 3.4 1.6 2.9 -0.1 42 0.3 1.0 2.7 -0.7
43 0.1 3.3 0.9 0.6 44 1.8 0.5 3.2 -0.7
45 1.9 0.1 0.6 -0.5 46 1.8 0.5 3.0 -0.4
47 3.0 0.1 0.8 -0.9 48 3.1 1.6 3.0 0.1
49 3.1 2.5 1.9 0.9 50 2.1 2.8 2.9 -0.4
51 2.3 1.5 0.4 0.7 52 3.3 0.6 1.2 -0.5
53 0.3 0.4 3.3 0.7 54 1.1 3.0 0.3 0.7
55 0.5 2.4 0.9 0.0 56 1.8 3.2 0.9 0.1
57 1.8 0.7 0.7 0.7 58 2.4 3.4 1.5 -0.1
59 1.6 2.1 3.0 -0.3 60 0.3 1.5 3.3 -0.9
61 0.4 3.4 3.0 -0.3 62 0.9 0.1 0.3 0.6
63 1.1 2.7 0.2 -0.3 64 2.8 3.0 2.9 -0.5
65 2.0 0.7 2.7 0.6 66 0.2 1.8 0.8 -0.9
67 1.6 2.0 1.2 -0.7 68 0.1 0.0 1.1 0.6
69 2.0 0.6 0.3 0.2 70 1.0 2.2 2.9 0.7
71 2.2 2.5 2.3 0.2 72 0.6 2.0 1.5 -0.2
73 0.3 1.7 2.2 0.4 74 0.0 2.2 1.6 -0.9
75 0.3 0.4 2.6 0.2
;
proc robustreg data=hbk fwls method=lts;
model y = x1 x2 x3 / diagnostics leverage;
id index;
run;
