/*,hi,buddy,i wish it may works. The SAMPLE LIBRARY  of SAS,good luck!!*/

  /****************************************************************/
  /*          S A S   S A M P L E   L I B R A R Y                 */
  /*                                                              */
  /*    NAME: PLSEG1                                              */
  /*   TITLE: Partial Least Squares Analysis                      */
  /* PRODUCT: STAT                                                */
  /*  SYSTEM: ALL                                                 */
  /*    KEYS: PLS, regression analysis, chemometrics              */
  /*   PROCS: PLS                                                 */
  /*    DATA:                                                     */
  /*                                                              */
  /*     REF:                                                     */
  /*    MISC:                                                     */
  /*                                                              */
  /****************************************************************/
   /*
 /  A certain chemical process involves five different reactions.  For
 /  20 different runs of the process, reaction time, temperature, and
 /  pressure as well as chemical yield are observed for each substi-
 /  tuent reaction.  The following data step reads in the data.
 /---------------------------------------------------------------------*/
 data process;
    input time1-time5 temp1-temp5 pres1-pres5 yield1-yield5;
    cards;
  6.4 3.6 4.0 6.2 4.4
    7  11  48  19   7
  .19 .48 .17 .09 .15      37.9  99.6  88.9  54.3  73.4
  7.9 7.1 7.7 9.9 5.5
   22  33  60  54  34
  .46 .74 .51 .26 .43      62.9 159.8 130.5  88.3 106.3
  3.2 1.1 1.6 2.5 2.1
    5  12  25   0   6
  .04 .21 .05 .00 .03      17.4  44.6  36.6  21.5  34.2
  4.3 2.6 2.2 4.3 3.0
    0   0  31  20   0
  .12 .32 .06 .11 .08      25.9  67.0  57.8  38.4  54.0
  5.5 7.4 8.7 9.3 3.7
   39  62  43  62  63
  .55 .66 .73 .30 .58      62.6 146.5 117.1  83.3  84.4
  2.1 2.8 2.2 3.3 1.6
    5   9  15  32  12
  .17 .23 .15 .18 .15      18.7  50.8  36.3  25.7  36.5
  6.2 6.8 8.7 9.2 4.1
   39  65  49  49  61
  .51 .67 .71 .22 .54      59.2 150.7 116.2  84.4  88.2
  4.2 2.5 2.9 4.2 2.8
    8  14  32  15  11
  .14 .33 .15 .07 .12      26.6  67.9  60.5  40.6  51.3
  7.4 5.8 6.9 8.7 5.0
   23  36  57  36  32
  .37 .65 .45 .16 .36      55.1 143.3 114.1  80.5  97.9
  5.8 4.4 5.5 6.8 3.9
   20  34  45  26  29
  .28 .51 .36 .11 .28      41.2 110.1  88.0  56.9  74.3
  7.1 7.3 8.7 9.9 4.8
   34  54  55  55  52
  .52 .73 .66 .26 .52      60.7 161.2 127.1  90.4 100.2
  7.7 5.7 8.0 9.0 5.0
   33  57  61  28  47
  .40 .68 .57 .10 .42      61.7 147.7 120.0  82.9  96.7
  4.4 4.3 4.5 5.9 3.1
   14  22  33  36  23
  .28 .43 .30 .19 .26      38.4  96.5  73.6  50.2  63.7
  1.1 2.8 1.6 2.8 1.0
    3   3   7  40  10
  .18 .18 .12 .23 .14      16.8  38.9  34.8  21.2  23.7
  7.6 6.6 7.1 9.5 5.3
   20  30  58  48  30
  .42 .70 .46 .23 .39      59.3 152.1 123.6  82.9 103.0
  0.0 0.0 0.0 0.0 0.0
    3   9   0   4   8
  .00 .00 .00 .03 .00       0.0   0.0   0.0   0.0   0.0
  2.9 1.7 2.1 2.9 2.0
    7  14  22  10  11
  .10 .23 .11 .05 .08      21.9  48.0  37.5  24.9  37.7
  4.1 2.5 3.6 4.2 2.6
   15  29  32  10  22
  .16 .33 .23 .04 .16      23.3  68.6  57.7  39.1  48.4
  2.4 3.0 3.0 3.8 1.7
   12  20  18  31  22
  .21 .27 .23 .16 .20      24.8  58.2  47.9  30.5  36.3
  3.8 1.9 3.0 3.6 2.4
   13  26  30   4  19
  .11 .29 .18 .01 .12      23.6  62.8  50.1  33.2  47.6
 ;

  /*
 /  You can use the method of partial least squares to model the
 /  yields as a function of all the reaction variables.  The following
 /  statements print a table which summarizes how much variation each
 /  PLS component accounts for.
 /---------------------------------------------------------------------*/
 proc pls data=process;
    model yield1-yield5 = time1-time5 temp1-temp5 pres1-pres5;
    run;

  /*
 /  Notice that the percentage of variation in Y accounted for by the
 /  PLS analysis doesn't change very much after the first few compo-
 /  nents.  You can use the CV=ONE option to select number of compo-
 /  nents by cross-validation.
 /---------------------------------------------------------------------*/
 proc pls data=process cv=one;
    model yield1-yield5 = time1-time5 temp1-temp5 pres1-pres5;
    run;

  /*
 /  While three PLS components give the absolute minimum predicted
 /  residual sum of squares of 0.76 for the cross-validation, this
 /  isn't very different from the PRESS of 0.82 for only two PLS
 /  components.  You can use the CVTEST option to test for the
 /  significance of this difference.
 /---------------------------------------------------------------------*/
 proc pls data=process cv=one cvtest;
    model yield1-yield5 = time1-time5 temp1-temp5 pres1-pres5;
    run;

  /*
 /  The result is that three PLS components don't explain signifi-
 /  cantly more variation than just two.*/