*Bayesian Analysis Module II examples: *Data input; /* BMI =Body Mass Index Age =Age, in Years Time =Time Since Diagnosis of Disease, in Weeks BCM =Two Biochemical Markers (each classified as normal=1 or abnormal=0) AHBA =Anti Hepatitis B Antigen Jaund =Associated Jaundice (yes=1, no=0) LC_nodes =Number of Cancerous Liver Nodes */ DATA Liver; input BMI Age Time BCM AHBA Jaund LC_nodes; datalines; 19.1358 50.0110 51.000 0 0 1 3 23.5970 18.4959 3.429 0 0 1 9 20.0474 56.7699 3.429 1 1 0 6 28.0277 59.7836 4.000 0 0 1 6 28.6851 74.1589 5.714 1 0 1 1 18.8092 31.0630 2.286 0 1 1 61 28.7201 52.9178 37.286 1 0 1 6 21.3669 61.6603 54.143 0 1 1 6 23.7332 42.2904 0.571 1 0 1 21 20.4783 22.1260 19.000 1 0 1 6 22.8625 25.2164 1.714 0 1 1 6 22.0932 66.7562 2.571 0 0 1 1 24.3141 66.8000 26.714 1 1 0 2 21.4619 78.9863 9.714 0 0 1 6 23.8087 58.3260 2.000 0 1 1 6 19.3698 48.4904 2.000 1 1 1 6 23.4568 70.9890 1.429 0 0 1 6 24.4418 70.7425 5.714 1 0 1 6 22.9130 49.7041 13.143 1 0 1 6 22.5306 64.0438 4.143 1 1 1 6 32.7449 62.2082 0.143 1 1 0 3 20.0617 22.7671 0.143 1 1 1 6 15.9597 48.8137 1.571 1 0 1 6 31.4398 64.5918 63.143 0 0 1 2 22.9854 79.5205 2.714 1 0 1 1 19.2653 37.8685 4.857 1 1 1 1 19.5313 65.0630 0.857 0 0 1 6 24.1415 39.9452 4.429 1 0 1 6 17.1225 13.9342 0.429 1 0 1 6 21.4692 64.9699 4.714 1 1 1 6 25.3515 52.8027 0.857 0 0 1 6 30.1194 65.2438 6.000 1 0 0 6 29.1749 47.0301 4.286 1 1 0 6 21.7784 71.5123 2.571 1 0 1 6 17.3010 57.8575 16.714 1 1 1 6 17.0068 68.0356 69.143 1 0 1 6 20.0000 48.4027 23.714 1 0 1 6 19.2653 62.5014 2.000 1 1 0 6 25.3815 58.1671 2.143 1 1 1 6 25.9151 53.2027 113.000 1 1 1 6 22.2656 59.8904 0.857 0 0 1 6 22.4600 65.7288 5.286 1 0 0 1 18.0092 24.2274 2.286 1 0 1 6 19.4708 28.3644 0.571 1 0 1 6 20.7612 68.9342 2.714 1 0 0 2 32.0313 59.9781 5.429 0 0 1 6 19.8413 45.4740 1.143 0 0 1 6 24.4898 43.5315 4.286 1 0 1 6 21.2585 49.6274 4.714 0 0 0 6 20.0155 52.1397 5.429 1 1 1 6 19.5682 41.3233 6.571 1 1 1 1 23.6614 74.7616 6.429 1 1 1 3 20.5693 78.1671 1.857 1 1 1 6 18.7652 17.7534 104.000 1 0 1 6 21.7738 32.7616 3.571 1 0 1 6 30.8532 62.6932 3.571 1 0 1 2 23.1481 44.1178 4.571 1 0 1 2 29.7576 60.1342 0.429 1 0 1 6 21.5619 41.9096 2.429 0 0 1 6 24.3046 62.8603 3.429 0 0 1 2 20.7248 66.9918 1.429 0 0 1 6 36.3880 55.3178 1.429 1 0 0 2 21.9076 49.8466 64.143 0 1 1 3 18.3058 72.7233 0.571 1 1 1 2 26.5118 75.7562 2.143 1 0 0 2 23.4236 49.1178 4.429 1 0 1 6 24.7245 61.0521 5.000 1 0 0 1 32.2421 65.8795 0.000 0 0 0 6 23.3556 71.2712 2.857 1 0 1 3 22.7732 68.7014 3.857 0 0 0 1 19.4870 63.6192 4.143 1 0 0 1 24.5390 56.3890 5.143 0 1 1 6 26.8977 60.3507 3.000 1 1 0 6 25.2595 72.9863 5.429 0 0 1 1 22.1297 77.5808 1.286 1 0 1 6 9.6849 49.6274 0.286 0 0 1 6 17.0068 12.6466 7.143 1 0 1 1 18.4240 59.8055 0.857 1 0 1 6 19.1406 68.1781 6.857 1 1 1 4 18.5078 70.5890 2.143 0 0 1 1 19.5965 66.7315 1.143 1 0 1 1 24.4418 60.2137 4.714 1 0 0 0 30.1194 61.8740 0.143 1 1 1 6 25.3444 38.3507 4.000 0 0 1 6 21.4844 68.7726 3.143 1 0 0 1 20.1995 66.9041 5.571 1 0 1 4 25.2994 62.8685 12.714 1 0 0 6 23.6013 70.3808 4.286 1 0 1 6 27.1706 62.3397 2.429 1 0 1 6 20.9024 62.9425 7.857 0 0 0 6 20.4491 73.7890 8.000 0 0 1 1 22.1510 55.4822 1.286 0 0 1 6 22.5710 75.0274 7.571 1 0 0 6 27.9904 76.4082 1.429 1 0 0 3 29.0688 54.9479 4.143 1 0 0 1 20.9184 60.2521 2.571 0 1 0 1 18.1940 37.1808 8.143 1 0 0 2 21.4536 24.8822 1.714 0 1 0 9 14.0445 61.3288 6.571 1 0 0 6 16.7311 60.3288 2.143 1 0 0 6 24.6094 42.9918 2.571 1 0 0 6 25.0829 54.4329 16.286 1 0 0 9 21.5510 58.6658 6.857 0 0 0 6 24.2215 75.7836 3.429 0 1 0 2 30.4498 69.8795 4.429 1 0 0 2 20.6790 39.7315 2.143 1 0 1 0 59.2554 41.1342 5.571 1 0 0 3 22.7244 60.2575 41.571 1 0 0 6 20.7008 75.3671 3.429 0 0 1 3 24.6094 47.3644 8.714 0 0 0 1 21.8300 74.4027 5.286 0 0 0 6 20.8980 66.1178 34.429 0 0 0 6 31.9602 69.6247 4.000 1 0 0 6 29.4107 45.4521 4.571 1 0 0 6 22.9421 65.4027 1.143 1 0 1 21 24.8163 67.1096 3.429 1 0 0 6 19.8178 65.9014 1.286 1 1 0 6 18.7783 61.0904 2.571 1 0 0 1 26.0617 55.4384 3.571 1 0 0 1 21.6333 61.5288 3.571 0 0 0 6 32.5260 71.4904 5.714 1 0 0 9 25.4028 68.2329 48.714 1 0 0 6 20.5693 29.2575 3.571 1 0 0 6 19.2570 33.1233 0.714 1 0 0 6 20.8980 40.2822 4.857 1 0 0 1 17.0562 30.2247 2.143 1 1 0 6 25.9924 66.5151 2.857 1 0 1 6 31.0735 73.0493 8.714 1 0 0 2 20.9840 48.2027 4.857 1 0 0 2 21.4536 69.1808 2.571 0 0 0 1 26.2346 60.3425 2.571 1 0 1 1 24.1633 60.8329 11.000 1 0 1 1 26.8519 58.6877 3.429 1 0 1 2 17.0993 48.8384 3.000 0 0 0 9 19.1327 65.3425 2.571 1 0 0 1 17.3010 51.4493 4.429 1 0 0 6 ; *T-test; DATA Iris; set sashelp.Iris; PROC FREQ data=Iris; tables Species; PROC MEANS data=Iris; where Species in ("Setosa" "Versicolor"); var SepalLength; by Species; PROC SGPLOT data=Iris; where Species in ("Setosa" "Versicolor"); histogram SepalLength /group=Species transparency=0.30; PROC GENMOD data=Iris; where Species in ("Setosa" "Versicolor"); class Species; model SepalLength=Species; PROC GENMOD data=Iris; where Species in ("Setosa" "Versicolor"); class Species; model SepalLength=Species; bayes seed=1 coeffprior=normal; *ANOVA; PROC SGPLOT data=Iris; vbox SepalLength /group=Species; PROC GENMOD data=Iris; class Species; model SepalLength=Species; PROC GENMOD data=Iris; class Species; model SepalLength=Species; bayes seed=1 coeffprior=uniform; *Linear Regression; PROC GLIMMIX data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Normal; PROC GENMOD data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Normal; PROC GENMOD data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Normal; bayes seed=1 coeffprior=normal; *Poisson Regression; PROC GLIMMIX data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Poisson; PROC GENMOD data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Poisson link=log; PROC GENMOD data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Poisson link=log; bayes seed=1 coeffprior=normal; DATA NormalPrior; input _type_ $ Intercept BMI Age Time BCM AHBA Jaund; datalines; Var 1e6 0.0005 1e6 1e6 1e6 1e6 1e6 Mean 0.0 0.1385 0.0 0.0 0.0 0.0 0.0 ; PROC GENMOD data=Liver; model LC_nodes = BMI Age Time BCM AHBA Jaund / dist=Poisson link=log; bayes seed=1 plots=none coeffprior=normal(input=NormalPrior); *Logistic Regression; PROC GLIMMIX data=Liver; model Jaund(Event='1') = BMI / dist=Binary; output out=pred pred(ilink) lcl(ilink) ucl(ilink); PROC SORT data=pred; by BMI; PROC SGPLOT data=pred; band x=BMI lower=LCLMu upper=UCLMu; scatter y=Jaund x=BMI; series y=PredMu x=BMI; PROC GENMOD data=Liver; model Jaund(Event='1') = BMI / dist=Binomial; PROC GENMOD data=Liver; model Jaund(Event='1') = BMI / dist=Binomial; bayes seed=1 plots=none coeffprior=uniform;