非常有用的sas程序，拿来即可用，从基本的数据整理到高级数据分析，应有尽有。


样例：

/*-------------------------------------------------------------------*/
/* Multiple Comparisons and Multiple Tests Using the SAS(r) System   */
/*                                                           */


/* Program 2.4: Bonferroni and Sidak Adjusted p-values Using the DATA Step */
data one;
   input test pval @@;
   bon_adjp = min(1,10*pval);
   sid_adjp = 1 - (1-pval)**10;
   datalines;
1 0.0911    2 0.8912
3 0.0001    4 0.5718
5 0.0132    6 0.9011
7 0.2012    8 0.0289
9 0.0498   10 0.0058
;
proc sort data=one out=one;
   by pval;
proc print data=one;
   run;

/* Program 2.5: Bonferroni and Sidak Adjusted p-values Using PROC MULTTEST */
data one; set one;
   rename pval=raw_p;
   drop bon_adjp sid_adjp;
proc multtest pdata=one bon sid out=outp;
proc sort data=outp out=outp;
   by raw_p;
proc print data=outp;
run;

/* Program 2.6: Conservative Simultaneous Confidence Intervals with Multivariate Data */
   data _null_;
      call symput('bonalpha',0.05/9           );
      call symput('sidalpha',1-(1-0.05)**(1/9));
   data HusbWive;
      input HusbQ1-HusbQ4 WifeQ1-WifeQ4 @@;
      DiffQ1 = HusbQ1-WifeQ1;
      DiffQ2 = HusbQ2-WifeQ2;
      DiffQ3 = HusbQ3-WifeQ3;
      DiffQ4 = HusbQ4-WifeQ4;
      DiffQAvg = sum(of HusbQ1-HusbQ4)/4 - sum(of WifeQ1-WifeQ4)/4;
      DiffComp = sum(of HusbQ1-HusbQ2)/2 - sum(of WifeQ1-WifeQ2)/2;
      DiffPass = sum(of HusbQ3-HusbQ4)/2 - sum(of WifeQ3-WifeQ4)/2;
      DiffFFP  = sum(of HusbQ1 HusbQ3)/2 - sum(of WifeQ1 WifeQ3)/2;
      DiffFFY  = sum(of HusbQ2 HusbQ4)/2 - sum(of WifeQ2 WifeQ4)/2;
    datalines;
   2 3 5 5   4 4 5 5      5 5 4 4   4 5 5 5      4 5 5 5   4 4 5 5
   4 3 4 4   4 5 5 5      3 3 5 5   4 4 5 5      3 3 4 5   3 3 4 4
   3 4 4 4   4 3 5 4      4 4 5 5   3 4 5 5      4 5 5 5   4 4 5 4
   4 4 3 3   3 4 4 4      4 4 5 5   4 5 5 5      5 5 4 4   5 5 5 5
   4 4 4 4   4 4 5 5      4 3 5 5   4 4 4 4      4 4 5 5   4 4 5 5
   3 3 4 5   3 4 4 4      4 5 4 4   5 5 5 5      5 5 5 5   4 5 4 4
   5 5 4 4   3 4 4 4      4 4 4 4   5 3 4 4      4 4 4 4   5 3 4 4
   4 4 4 4   4 5 4 4      3 4 5 5   2 5 5 5      5 3 5 5   3 4 5 5
   5 5 3 3   4 3 5 5      3 3 4 4   4 4 4 4      4 4 4 4   4 4 5 5
   3 3 5 5   3 4 4 4      4 4 3 3   4 4 5 4      4 4 5 5   4 4 5 5
   ;
   
   proc glm;
      model HusbQ1-HusbQ4 WifeQ1-WifeQ4 = / nouni;
      repeated Spouse 2, Question 4 identity;
      run;
   
   proc means alpha=0.05 n mean lclm uclm;
      title "Unadjusted Confidence Intervals";
      var DiffQ1-DiffQ4 DiffQAvg DiffComp DiffPass DiffFFP DiffFFY;
   proc means alpha=&sidalpha n mean lclm uclm;
      title "Simultaneous Sidak Intervals";
      var DiffQ1-DiffQ4 DiffQAvg DiffComp DiffPass DiffFFP DiffFFY;
   proc means alpha=&bonalpha n mean lclm uclm;
      title "Simultaneous Bonferroni Intervals";
      var DiffQ1-DiffQ4 DiffQAvg DiffComp DiffPass DiffFFP DiffFFY;
   run;
   
/* Program 2.7: Multiple Tests with Multivariate Data */
proc means data=HusbWive n mean std prt;
   title "Tests of Hypotheses With Husband/Wife Data";
   var DiffQ1-DiffQ4 DiffQAvg DiffComp DiffPass DiffFFP DiffFFY;
run;

/* Program 2.8: HOLM Adjusted p-values Using PROC MULTTEST */
data one;
   set one;
   rename pval=raw_p;
   drop bon_adjp sid_adjp;
proc multtest pdata=one bon stepbon out=outp;
proc sort data=outp out=outp;
   by raw_p;
proc print data=outp;
run;
/* Program 2.9. Sidak-Holm Adjusted p-values Using PROC MULTTEST */
data one; set one;
   rename pval=raw_p;
   drop bon_adjp sid_adjp;
proc multtest pdata=one sid stepsid out=outp;
proc sort data=outp out=outp;
   by raw_p;
proc print data=outp;
run;