/* ODS TRACE and ODS SELECT                */

data hsb2;
  input  id female race ses prog
         read write math science socst;
datalines;
 70 0 4 1 1 57 52 41 47 57
121 1 4 2 3 68 59 53 63 61
 86 0 4 3 1 44 33 54 58 31
141 0 4 3 3 63 44 47 53 56
172 0 4 2 2 47 52 57 53 61
113 1 4 2 2 44 52 51 63 61
 50 0 3 2 1 50 59 42 53 61
 11 0 1 2 2 34 46 45 39 36
 84 0 4 2 1 63 57 54 51 63
 48 1 3 2 2 57 55 52 50 51
 75 1 4 2 3 60 46 51 53 61
 60 1 4 2 2 57 65 51 63 61
 95 0 4 3 2 73 60 71 61 71
104 0 4 3 2 54 63 57 55 46
 38 0 3 1 2 45 57 50 31 56
115 0 4 1 1 42 49 43 50 56
 76 0 4 3 2 47 52 51 50 56
195 0 4 2 1 57 57 60 56 52
;
ODS TRACE ON;
proc glm data=hsb2;
model read write math science socst=female race ses prog;
*ODS SELECT GLM.Data.NOBS GLM.ANOVA.socst.OverallANOVA;
*ODS OUTPUT OverallANOVA=OutANOVA GLM.ANOVA.socst.OverallANOVA=OutSOCSTANOVA 
  GLM.Data.NOBS=SampleSize NOBS=SampleSize2;
run;
ods trace off;

/* Working with the extracted output data */

proc print data= OutSOCSTANOVA;
run;

proc print data= SampleSize;
run;

data myss;
set OutSOCSTANOVA (KEEP=SS);
run;

proc print data=myss;
run;

/******************************/


proc sort data=hsb2;
by race;

ODS TRACE ON;
proc means data=hsb2;
by race;
*ODS SELECT 	Means.ByGroup3.Summary;
run;
ods trace off;


/******************************/


data hsb2;
  input  id female race ses prog
         read write math science socst;
datalines;
 70 0 4 1 1 57 52 41 47 57
121 1 4 2 3 68 59 53 63 61
 86 0 4 3 1 44 33 54 58 31
141 0 4 3 3 63 44 47 53 56
172 0 4 2 2 47 52 57 53 61
113 1 4 2 2 44 52 51 63 61
 50 0 3 2 1 50 59 42 53 61
 11 0 1 2 2 34 46 45 39 36
 84 0 4 2 1 63 57 54 51 63
 48 1 3 2 2 57 55 52 50 51
 75 1 4 2 3 60 46 51 53 61
 60 1 4 2 2 57 65 51 63 61
 95 0 4 3 2 73 60 71 61 71
104 0 4 3 2 54 63 57 55 46
 38 0 3 1 2 45 57 50 31 56
115 0 4 1 1 42 49 43 50 56
 76 0 4 3 2 47 52 51 50 56
195 0 4 2 1 57 57 60 56 52
;
ODS TRACE ON;
proc glm data=hsb2;
model read write math science socst=female race ses prog;
*ODS OUTPUT     
GLM.ANOVA.read.ModelANOVA =readANOVA 
 GLM.ANOVA.write.ModelANOVA =writeANOVA
GLM.ANOVA.math.ModelANOVA = mathANOVA
GLM.ANOVA.science.ModelANOVA = sciANOVA
GLM.ANOVA.socst.ModelANOVA = socstANOVA ;
run;
ods trace off;

data allANOVA;
set readANOVA writeANOVA mathANOVA sciANOVA socstANOVA;
run;

proc print data=allANOVA;
run;

data type3pvals;
set allANOVA;
if HypothesisType=3;
run;

proc print data=type3pvals;
run;

data type3pvals;
set type3pvals  (KEEP = ProbF);
run;

proc print data=type3pvals;
run;




/* Another example */

DATA surg;
INPUT x1 x2 x3 x4 x5 x6 x7 x8 y lny;
 label x1 = 'blood-clotting'
 x2 = 'prognostic'
 x3 = 'enzyme'
 x4 = 'liver function'
 x5 = 'age'
 x6 = 'gender'
 x7 = 'moderate alcohol use'
 x8 = 'heavy alcohol use'
 y = 'survival'
 lny = 'log survival';
cards;
6.7 62 81 2.59 50 0 1 0 695 6.544
5.1 59 66 1.70 39 0 0 0 403 5.999
7.4 57 83 2.16 55 0 0 0 710 6.565
6.5 73 41 2.01 48 0 0 0 349 5.854
7.8 65 115 4.30 45 0 0 1 2343 7.759
5.8 38 72 1.42 65 1 1 0 348 5.852
5.7 46 63 1.91 49 1 0 1 518 6.250
3.7 68 81 2.57 69 1 1 0 749 6.619
6.0 67 93 2.50 58 0 1 0 1056 6.962
3.7 76 94 2.40 48 0 1 0 968 6.875
6.3 84 83 4.13 37 0 1 0 745 6.613
6.7 51 43 1.86 57 0 1 0 257 5.549
5.8 96 114 3.95 63 1 0 0 1573 7.361
5.8 83 88 3.95 52 1 0 0 858 6.754
7.7 62 67 3.40 58 0 0 1 702 6.554
7.4 74 68 2.40 64 1 1 0 809 6.695
6.0 85 28 2.98 36 1 1 0 682 6.526
3.7 51 41 1.55 39 0 0 0 205 5.321
7.3 68 74 3.56 59 1 0 0 550 6.309
5.6 57 87 3.02 63 0 0 1 838 6.731
5.2 52 76 2.85 39 0 0 0 359 5.883
3.4 83 53 1.12 67 1 1 0 353 5.866
6.7 26 68 2.10 30 0 0 1 599 6.395
5.8 67 86 3.40 49 1 1 0 562 6.332
6.3 59 100 2.95 36 1 1 0 651 6.478
5.8 61 73 3.50 62 1 1 0 751 6.621
5.2 52 86 2.45 70 0 1 0 545 6.302
11.2 76 90 5.59 58 1 0 1 1965 7.583
5.2 54 56 2.71 44 1 0 0 477 6.167
5.8 76 59 2.58 61 1 1 0 600 6.396
3.2 64 65 0.74 53 0 1 0 443 6.094
8.7 45 23 2.52 68 0 1 0 181 5.198
5.0 59 73 3.50 57 0 1 0 411 6.019
5.8 72 93 3.30 39 1 0 1 1037 6.944
5.4 58 70 2.64 31 1 1 0 482 6.179
5.3 51 99 2.60 48 0 1 0 634 6.453
2.6 74 86 2.05 45 0 0 0 678 6.519
4.3 8 119 2.85 65 1 0 0 362 5.893
4.8 61 76 2.45 51 1 1 0 637 6.457
5.4 52 88 1.81 40 1 0 0 705 6.558
5.2 49 72 1.84 46 0 0 0 536 6.283
3.6 28 99 1.30 55 0 0 1 582 6.366
8.8 86 88 6.40 30 1 1 0 1270 7.147
6.5 56 77 2.85 41 0 1 0 538 6.288
3.4 77 93 1.48 69 0 1 0 482 6.178
6.5 40 84 3.00 54 1 1 0 611 6.416
4.5 73 106 3.05 47 1 1 0 960 6.867
4.8 86 101 4.10 35 1 0 1 1300 7.170
5.1 67 77 2.86 66 1 0 0 581 6.365
3.9 82 103 4.55 50 0 1 0 1078 6.983
6.6 77 46 1.95 50 0 1 0 405 6.005
6.4 85 40 1.21 58 0 0 1 579 6.361
6.4 59 85 2.33 63 0 1 0 550 6.310
8.8 78 72 3.20 56 0 0 0 651 6.478
;
RUN;


/* We fit the model with only x1, x2, x3, x8 as predictors: */

ODS TRACE ON;
PROC REG DATA = surg;
MODEL lny = x1 x2 x3 x8 / vif influence R;
* ODS SELECT Reg.MODEL1.Fit.lny.ParameterEstimates Reg.MODEL1.ObswiseStats.lny.OutputStatistics;
* ODS OUTPUT Reg.MODEL1.Fit.lny.ParameterEstimates=OutParams Reg.MODEL1.ObswiseStats.lny.OutputStatistics=OutInfluence;
RUN;
ODS TRACE OFF;

/* Using the DFFITS measure to pick out influential observations */

DATA influ;
SET OutInfluence;
p=5;n=54;
if abs(dffits) > 2*((p/n)**0.5);
run;
PROC PRINT data=influ;
VAR observation dffits;
run;