SAS讲课程序文件汇总

1、目录SAS基础3创建数据集3数据集的导入（excel）3数据集的排序（默认升序）3计算产生新变量4数据集的拆分4数据集的合并5纵向合并5横向合并5计量资料的描述6计算几何均数6制作频数表6单变量描述7多变量描述8两样本均数的比较9单一总体均数的可信区间（原始数据未知）9两总体均数相差可信区间（原始数据未知）9单样本均数的t检验（原始数据未知）10单样本均数的t检验（原始数据已知）10Ttest过程10配对资料两样本均数比较的t检验10MEANS过程10UNIVARIATE过程11TTest过程11独立资料两样本均数比较的t检验（原始数据已知ttest过程）11独立资料两样本均数比较的t检验（t

2、test过程-循环语句）12独立资料两样本均数比较的t检验（原始数据未知）12多个样本均数比较的方差分析13完全随机设计的方差分析13随机区组设计的方差分析14析因资料的方差分析14相关与回归分析15绘制散点图15绘制分层散点图16Pearson相关系数及其置信区间的计算17Spearman等级相关系数及其置信区间的计算17简单线性回归方程的估计及假设检验17绘制回归直线置信带、预测带18残差图绘制18多变量回归分析19多元回归绘制残差图19自变量筛选19卡方检验20独立四格表资料的卡方检验（freq过程）20配对四格表资料率的比较的卡方检验21R*C表资料的卡方检验22关联性分析22基于秩次

3、的非参数检验23单样本的符号秩和检验（univariate过程）23配对样本的符号秩和检验（univariate过程）24两独立样本的秩和检验（NPAR1WAY+ wilcoxon）24多组独立样本的秩和检验（NPAR1WAY+ wilcoxon）25随机区组样本的秩和检验（freq）26Logistic回归与生存分析27Logistic回归27二分类Logistic回归(logistic过程)27有序LOGISTIC回归28多分类LOGISTIC回归29生存分析30寿命表方法进行生存率的估计30K-M法进行生存率的估计31Cox回归（phreg）32匹配Logistic回归32SAS基础创建

4、数据集DATA A;INPUT ID GENDER $ AGE BLOOD $ SURT;/*ID 等为变量名，$表示其前面的那个变量为字符型变量*/DATALINES; /*引出数据行*/1M41A3682M26B7453F35B4014M47AB5525F37A4786F39O6287M28O5498M31B1289M43AB46310M29A512;ODS HTML FILE=E:RESULT.xls; /* 将以下SAS输出结果存放到EXCEL文件中*/PROC PRINT DATA=A;RUN;ODS HTML CLOSE;/*关闭对外输出系统*/数据集的导入（excel）Proc

5、import datafile=E:data1-1.xls dbms=excel replace out=a;Run;数据集的排序（默认升序）PROC FORMAT;VALUE GE 1=M 2=F; /*定义值标签*/VALUE BL 1=A 2=B 3=AB 4=O;DATA A;INPUT ID GENDER AGE BLOOD SURT;DATALINES;114113682126274532352401414735525237147862394628712845498131212891433463101291512;RUN;ODS HTML FILE=E:RESULT.xls ;PR

6、OC PRINT DATA=A;FORMAT GENDER GE.; /*借用值标签*/FORMAT BLOOD BL.;RUN;ODS HTML CLOSE;Proc sort data=a out=b; by age;Run;Proc sort data=a out=c; by descending age;Run;计算产生新变量DATA D; FORMAT SURT_Y 5.3;SET A; SURT_Y=SURT/365;IF AGE40 THEN AGROUP=1;ELSE AGROUP=2;RUN;数据集的拆分DATA A;INPUT ID GENDER $ AGE BLOOD $

7、 SURT;DATALINES; /*引出数据行*/1M41A3682M26B7453F35B4014M47AB5525F37A4786F39O6287M28O5498M31B1289M43AB46310M29A512;DATA B C;SET A;IF GENDER=F THEN OUTPUT B;ELSE OUTPUT C;RUN;DATA B(DROP=GENDER) C(DROP=BLOOD);SET A;RUN;数据集的合并纵向合并data st1;input x y ; /* 表示连续读入数据 */cards;1 20 2 40;proc print;data st2;input

8、x y ;cards;3 80 4 160;proc print;data st;set st1 st2;proc print;run;横向合并data a;input num x ;cards;101 12 102 14 103 15；proc sort out=aa;by num;data b;input num y ;cards;101 100 102 134 103 145；proc sort out=bb;by num;data c;merge aa bb;by num;proc print;run;计量资料的描述计算几何均数data ex2_5; input x f ; y=log

9、10(x);cards;10 420 340 1080 10160 11320 15640 141280 2;proc means noprint; var y; freq f; output out=b mean=logmean;run;data c; set b; g=10*logmean;proc print data=c; var g;run;制作频数表data ex2_1; input x ; low=3.07; dis=0.2; z=x-mod(x-low,dis);cards;3.96 4.23 4.42 3.59 5.12 4.02 4.32 3.72 4.76 4.16 4.

10、61 4.26 3.77 4.20 4.36 3.07 4.89 3.97 4.28 3.64 4.66 4.04 4.55 4.25 4.63 3.91 4.41 3.52 5.03 4.01 4.30 4.19 4.75 4.14 4.57 4.26 4.56 3.79 3.89 4.21 4.95 3.98 4.29 3.67 4.69 4.12 4.56 4.26 4.66 4.28 3.83 4.20 5.24 4.02 4.33 3.76 4.81 4.17 3.96 3.27 4.61 4.26 3.96 4.23 3.76 4.01 4.29 3.67 3.39 4.12 4.

11、27 3.61 4.98 4.24 3.83 4.20 3.71 4.03 4.34 4.69 3.62 4.18 4.26 4.36 5.28 4.21 4.42 4.36 3.66 4.02 4.31 4.83 3.59 3.97 3.96 4.49 5.11 4.20 4.36 4.54 3.72 3.97 4.28 4.76 3.21 4.04 4.56 4.25 4.92 4.23 4.47 3.60 5.23 4.02 4.32 4.68 4.76 3.69 4.61 4.26 3.89 4.21 4.36 3.42 5.01 4.01 4.29 3.68 4.71 4.13 4.

12、57 4.26 4.03 5.46 4.16 3.64 4.16 3.76 ;proc freq; tables z;run;单变量描述/*用MEANS过程计算例数、均数、标准差、标准误*/proc means data=ex2_1;var x;run;/*用MEANS过程计算例数、均数、标准差、标准误和95%可信区间*/proc means data=ex2_1 n mean std stderr cv clm;var x;run;/*用MEANS过程对频数表计算例数、均数、标准差、最小值和最大值*/data prg4_4;input x f ;cards;3.17 23.37 33.57

13、93.77 143.97 224.17 304.37 214.57 154.77 104.97 65.17 45.37 2;proc means; freq f; var x;run;proc means maxdec=2 data=prg4_4; freq f; var x;run;多变量描述/*用UNIVARIATE过程进行描述性统计*/proc univariate data=ex2_1; freq f; var x;run;/*计算2.5%和97.5%分位数*/proc univariate data=ex2_1; freq f; var x; output out=pct pctlp

14、re=p pctlpts=2.5 97.5;run;proc print data=pct;run;/*作正态性检验、茎叶图、箱式图和正态概率图*/proc univariate data=ex2_1 Normal plot; var x;run;两样本均数的比较单一总体均数的可信区间（原始数据未知）data prg5_1; n=10; mean=166.95; std=3.64; t=tinv(0.975,n-1); in=t*std/sqrt(n); lclm=mean-in; uclm=mean+in;proc print; var lclm uclm;run;两总体均数相差可信区间（原

15、始数据未知）data prg5_2; n1=29; n2=32; m1=20.10; m2=16.89; s1=7.02; s2=8.46; ss1=s1*2*(n1-1); ss2=s2*2*(n2-1); sc2=(ss1+ss2)/(n1+n2-2); se=sqrt(sc2*(1/n1+1/n2); t=tinv(0.975,n1+n2-2); lclm=(m1-m2)-t*se; uclm=(m1-m2)+t*se;proc print; var t se lclm uclm;run;单样本均数的t检验（原始数据未知）data prg5_3; n=36; s_m=130.83; st

16、d=25.74; p_m=140; df=n-1; t=abs(s_m-p_m)/(std/sqrt(n); p=(1-probt(t,df)*2;proc print; var t p;run;单样本均数的t检验（原始数据已知）Ttest过程data prg5_4; input x ;datalines;171 79 135 78 118 175 122 105 111 140138 132 142 140 168 113 145 128 124134 116 129 155 135 134 136 113 119 132;proc ttest h0=140; var x;run;配对资料两

17、样本均数比较的t检验MEANS过程/*用MEANS作配对资料两个样本均数比较的t检验*/data prg5_5; input x1 x2 ; d=x1-x2;cards;0.840 0.5800.591 0.5090.674 0.5000.632 0.3160.687 0.3370.978 0.5170.750 0.4540.730 0.5121.200 0.9970.870 0.506;proc means n mean std stderr t prt; var d;run;UNIVARIATE过程/*用UNIVARIATE过程作配对资料两样本均数比较的t检验*/proc univaria

18、te data=prg5_5; var d;run;TTest过程/*用TTest过程作配对资料两样本均数比较的t检验*/proc ttest data=prg5_5; var d;run;独立资料两样本均数比较的t检验（原始数据已知ttest过程）data prg5_6; input x c ; datalines;-0.71 -5.61 21 2.81 0.713.51 41 5.81 7.11 -0.512.51 -1.61 1.71 31 0.414.51 4.61 2.51 61 -1.413.72 6.52 52 5.22 0.820.22 0.62 3.42 6.62 -1.12

19、62 3.82 22 1.62 222.22 1.22 3.12 1.72 -22 ;proc ttest; var x; class c;run;独立资料两样本均数比较的t检验（ttest过程-循环语句）data prg5_7; input x ; if _n_21 then c=1; else c=2;cards;-0.70 -5.60 2.00 2.80 0.70 3.50 4.00 5.80 7.10 -0.50 2.50 -1.60 1.70 3.00 0.40 4.50 4.60 2.50 6.00 -1.40 3.70 6.50 5.00 5.20 0.80 0.20 0.60

20、3.40 6.60 -1.10 6.00 3.80 2.00 1.60 2.00 2.20 1.20 3.10 1.70 -2.00;proc ttest; var x; class c;run;独立资料两样本均数比较的t检验（原始数据未知）data prg5_8; n1=102; n2=100; m1=0.87; m2=0.48; s1=0.89; s2=0.82; delta=0.52; ss1=s1*2*(n1-1); ss2=s2*2*(n2-1); sc2=(ss1+ss2)/(n1+n2-2); se=sqrt(sc2*(1/n1+1/n2); t=(m1-m2-delta)/se

21、; p=probt(t,n1+n2-2)*2;proc print; var t p;run;多个样本均数比较的方差分析完全随机设计的方差分析libname c D:;data c.t1;do c=1 to 4;do i=1 to 30;input x ;output;end;end;datalines;3.534.594.342.663.593.132.642.563.503.253.304.043.533.563.854.073.523.934.192.961.373.932.332.984.003.552.964.34.162.592.423.364.322.342.682.951.56

22、3.111.811.771.982.632.862.932.172.722.652.222.902.972.362.562.522.272.983.722.803.574.022.312.862.282.392.282.482.283.212.232.322.682.662.322.613.642.583.652.663.682.653.023.482.422.412.663.292.703.042.811.971.680.891.061.081.271.631.891.192.172.281.721.981.742.163.372.971.690.942.112.812.521.312.51

23、1.881.413.191.922.471.022.103.71;ods html file=D:1.xls;proc anova;class c;model x=c;means c;means c/lsd;run;随机区组设计的方差分析libname c D:;data c.t2;do a=1 to 5;do b=1 to 3;input x;output;end;end;cards;0.820.650.510.730.540.230.430.340.280.410.210.310.680.430.24;ods html file=D:2.xls;proc anova;class a b;m

24、odel x=a b;means b/snk;run;析因资料的方差分析libname c D:;data c.t3;do a=1 to 2;do b=1 to 2;do i=1 to 5;input x;output;end;end;end;cards;10 10 40 50 1030 30 70 60 3010 20 30 50 3050 50 70 60 30;ods html file=D:3.xls;proc anova;class a b;model x=a b a*b;run;相关与回归分析绘制散点图libname c D: ;/*定义关联库*/data c.t1;label x

25、=Height(cm) y=Weight(Kg);input x y;datalines;149.4 30.8 167.642.6 146.3 33.1 170.7 44.0161.5 36.3 164.6 40.8 155.5 32.7 158.5 35.4149.4 33.1 152.4 31.8;Goptions htext=0.25in ctext=black device=gif;AXIS1 label=(h=0.25in f=zapfbi j=rHEIGHT(cm)offset=(0.2in)order=(145to 175 by 10);AXIS2 label=(a=90h=0.

26、25in f=zapfbi j=lWEIGHT(Kg)order=(30 to 45 by 5)offset=(0.2in);/*/*定义坐标轴的格式*/*h=0.25in定义坐标轴标目的字体大小0.25inche*/*f=zapfbi定义坐标轴标目的字体zapfbi*/*j=r定义坐标轴标目靠右对齐*/*HEIGHT(cm)定义坐标轴标目为HEIGHT(cm)*/*offset=0.2in定义坐标轴起点处空0.2inche*/*order=(145to175by10)定义坐标轴刻度*/*/Ods html path=d: body=散点图.xls;proc gplot data=c.t1;p

27、lot y*x/HAXIS=AXIS1 VAXIS=AXIS2 noframe;symbol h=0.25in c=black;run;/*/*调用gplot过程绘制散点图*/*plot定义变量*/*Haxis定义横坐标轴的格式*/*Vaxis定义纵坐标轴的格式*/*noframe定义散点图无框*/*SYMBOL定义数据点的格式*/*h=0.25in定义数据点的大小为0.25inche*/*c=black定义数据点的颜色为黑色*/*/ods html close;绘制分层散点图data c.t2;label x=Height(cm) y=Weight(Kg);input x y;IF _N_=

28、5 THEN G=1;ELSE G=2;datalines;149.4 30.8 167.6 42.6 146.3 33.1 170.7 44.0161.5 36.3 164.6 40.8 155.5 32.7 158.5 35.4149.4 33.1 152.4 31.8;/* 建立具有分层变量的数据*/goptions htext=0.25in ctext=black device=gif;AXIS1 label=( h=0.25in f=zapfbi j=r HEIGHT(cm) offset=(0.2 in ) order=(145 to 175 by 10); AXIS2 label

29、=( a=90 h=0.25in f=zapfbi j=r WEIGHT(Kg) order=(30 to 45 by 5) offset=(0.2 in ) ; ods html path=d: body=分层散点图.xls;proc gplot data=c.t2 ;plot y*x=G/HAXIS=AXIS1 VAXIS=AXIS2 noframe;symbol1 h=0.25in c=black v=plus;symbol2 h=0.25in c=red v=plus;run;/*/*绘制分层的散点图*/*y*x=G定义以变量G为分层变量*/*用两个symbol语句定义两层数据点的格式

30、*/*/ODS HTML CLOSE;Pearson相关系数及其置信区间的计算 ods HTML FILE=D:RESULT.xls ;proc corr data=c.t1 fisher;var x y;run;ODS HTML CLOSE; /*/* 计算Pearson相关系数并将结果输出到EXCEL文件中*/* fisher指定要计算Fisher Z变换计算的总体相关系数的置信区间 */* var定义进行相关分析的两个变量*/*/Spearman等级相关系数及其置信区间的计算 data c.t3;label x=血红蛋白含量 y=贫血体征;input x y ;cards;5.0 4 5

31、.836.1 27.3 18.839.1311.1 112.3 113.5 113.8 1;ods HTML FILE=D:RESULT2.xls ; proc corr data=c.t3 spearman;var x y;run;ODS HTML CLOSE;/*/* 计算Spearman相关系数并将结果输出到EXCEL文件中*/* spearman指定要计算Spearman相关系数 */* var定义进行相关分析的两个变量*/*/;简单线性回归方程的估计及假设检验 libname c D:;data c.t1;input x y;datalines;149.4 30.8 167.642.

32、6 146.3 33.1 170.7 44.0161.5 36.3 164.6 40.8 155.5 32.7 158.5 35.4149.4 33.1 152.4 31.8; ods HTML path=D: body=残差图.xls;proc reg data=c.t1;model y=x/stb;plot student.*predicted.;output out=c.r p=yhat r=re student=rs lcl=ly ucl=uy lclm=lybar uclm=uybar ;run;ODS HTML CLOSE;quit;绘制回归直线置信带、预测带goptions ht

33、ext=0.25in ctext=black;symbol1 h=0.25in c=black i=rlclm95 v=plus;symbol2 h=0.25in c=red i=rlcli95;AXIS1 label=( h=0.25in f=zapfbi j=r HEIGHT(cm) offset=(0.2 in ) order=(145 to 175 by 10); AXIS2 label=( a=90 h=0.25in f=zapfbi j=r WEIGHT(Kg) order=(30 to 45 by 5) offset=(0.2 in ) ; proc gplot data=c.t

34、1;plot y*x y*x/HAXIS=AXIS1 VAXIS=AXIS2 noframe overlay ;run;残差图绘制goptions htext=0.25in ctext=black;symbol1 h=0.25in c=black i=n v=plus;AXIS1 label=( h=0.25in f=zapfbi j=r) offset=(0.2 in ) ; AXIS2 label=( a=90 h=0.25in f=zapfbi j=r) offset=(0.2 in ) ; proc gplot data=c.r;plot rs*y/haxis=axis1 vaxis=

35、axis2;run; /*/* 根据输出的残差数据绘制残差图 */* i=n定义不输出任何拟合线 */*/多变量回归分析libname c D: ;data c.t1;input BMI DY LEP FPG ADI;cards;24.2210.05.7513.629.3624.14 5.010.21 7.416.0124.22 3.09.32 6.214.3126.45 4.019.31 5.119.0319.0315.02.5011.126.0825.22 2.3 8.65 7.617.4623.39 3.05.66 9.719.6227.22 3.0 8.54 8.620.3619.49

36、 4.02.83 7.342.8225.93 6.0 7.21 8.915.9224.38 6.06.86 7.322.7626.9912.0 8.75 7.015.3419.03 2.93.22 7.731.0025.71 7.013.0713.5 8.0521.11 9.04.90 6.017.2828.41 4.0 8.9013.512.3123.32 5.03.54 6.730.2526.39 4.023.26 8.2 5.5924.34 2.04.51 7.224.2828.7310.019.05 6.9 8.5923.82 8.08.47 9.118.9427.4616.019.4

37、4 6.5 8.8922.8620.09.92 8.116.0827.9910.017.33 6.114.1024.4912.06.01 7.029.5028.41 2.014.59 6.811.7423.37 6.04.31 6.325.6430.69 1.522.06 8.1 5.1820.81 7.03.46 7.132.2629.39 3.020.56 7.5 6.12;goptions htext=0.25in ftext=宋体 ctext=black;ods html path=D: body=1.xls; proc reg data=c.t1;model ADI=BMI DY L

38、EP FPG /stb;plot student.*predicted.;output out=c.r p=yhat r=re student=rs lcl=ly ucl=uy lclm=lybar uclm=uybar ;run;多元回归绘制残差图AXIS1 label=( h=0.25in f=zapfbi j=r) offset=(0.2 in ) ; AXIS2 label=( a=90 h=0.25in f=zapfbi j=r) offset=(0.2 in ) ; proc gplot data=c.r;plot rs*yhat/haxis=axis1 vaxis=axis2;s

39、ymbol1 h=0.25in c=black i=n v=plus;run;自变量筛选ods HTML FILE=D:RESULT1.xls; proc reg data=c.t1;model ADI=BMI DY LEP FPG /stb selection=forward SLE=0.1 SLS=0.15（设定入选和排除标准）;run;ODS HTML CLOSE;quit;ods HTML FILE=DRESULT2.xls ; proc reg data=c.t1;mm1:model ADI=BMI DY LEP FPG /stb selection=forward;mm2:mode

40、l ADI=BMI DY LEP FPG /stb selection=backward;mm3:model ADI=BMI DY LEP FPG /stb selection=stepwise;mm4:model ADI=BMI DY LEP FPG /stb selection=rsquare;mm5:model ADI=BMI DY LEP FPG /stb selection=adjrsq;mm6:model ADI=BMI DY LEP FPG /stb selection=cp;run;ODS HTML CLOSE;quit;卡方检验独立四格表资料的卡方检验（freq过程）libn

41、ame c D: ;PROC FORMAT;VALUE G 1=万拉法新 2=氟西汀; /*定义值标签*/VALUE Lx 1=有效 2=无效;run;data c.t1;label R=组别 C=疗效;do r=1 to 2; do c=1 to 2; input f; output; end;end;datalines;19 1115 15;/*建立数据集并输入数据*/ODS HTML FILE=D:RESULT1.XLS;PROC freq DATA=c.t1;/* 调用FREQ过程进行卡方检验 */FORMAT r G.; /*借用值标签*/FORMAT c Lx.;table r*c

42、/chisq nopercent nocol expected; /* table /tables 后面定义要对哪些变量做交叉表，chisq表示要输出卡方检验的结果 */ /*nopercent 要求不要输出百分比，nocol要求不要输出列百分比，expected要求要输出理论频数 */weight f; /*定义按什么变量进行加权*/RUN;ODS HTML CLOSE;配对四格表资料率的比较的卡方检验PROC FORMAT;VALUE t 1=阳性 2=阴性;run;data c.t3;label R=甲法 C=乙法;do r=1 to 2; do c=1 to 2; input f; o

43、utput; end;end;datalines;80 1031 11;ODS html path=D: body=RESULT3.XLS;PROC freq DATA=c.t3;FORMAT r t.; FORMAT c t.;table r*c/agree nopercent nocol norow;/*agree表示要输出MecNemar检验的结果*/weight f;exact agree;run;ODS HTML CLOSE;R*C表资料的卡方检验PROC FORMAT;VALUE zy 1=干部 2=工人 3=农民 4=其他; VALUE mz 1=汉族 2=回族 3=满族;run

44、;data c.t4;label R=民族 C=职业;do r=1 to 3; do c=1 to 4; input f; output; end;end;datalines;20 56 62 714 40 32 1118 28 45 8 99;ODS HTML FILE=D:RESULT4.XLS;PROC freq DATA=c.t4;FORMAT r mz.; /*借用值标签*/FORMAT c zy.;table r*c/chisq nopercent nocol expected /*fisher定义要输出Fisher精确概率法的结果*/;weight f;RUN;ODS HTML

45、CLOSE;关联性分析PROC FORMAT;VALUE xx 1=O 2=A 3=B 4=AB; VALUE gj 1=美国 2=中国 3=挪威;run;data c.t6;label R=国籍 C=血型;do r=1 to 3; do c=1 to 4; input f; output; end;end;datalines;450 410 100 40300 250 350 100190 250 40 20;ODS HTML FILE=D: RESULT6.XLS;PROC freq DATA=c.t6;FORMAT r gj.; FORMAT c xx.;table r*c/chisq

46、nopercent nocol norow expected;weight f;RUN;ODS HTML CLOSE;基于秩次的非参数检验单样本的符号秩和检验（univariate过程）libname c D: ;data c.t1;input x;datalines;0.620.782.132.482.542.682.733.013.133.273.544.384.385.056.0811.27;/*建立数据集并输入数据*/ODS HTML FILE=D: RESULT1.XLS;PROC univariate DATA=c.t1 mu0=2.5;/* 调用 UNIVARIATE 过程进行单

47、个样本的秩和检验 */var x ; RUN;ODS HTML CLOSE;/* 练习2 */data c.t1_2;input x f;datalines;+34-14+25-22+16-3205;ODS HTML FILE=D:RESULT1_1.XLS;PROC univariate DATA=c.t1_2;/* 调用 UNIVARIATE 过程进行单个样本的秩和检验 */var x ;freq f; RUN;ODS HTML CLOSE;配对样本的符号秩和检验（univariate过程）data c.t2;input x1 x2;d=x1-x2;datalines; 10.5 8.8

48、21.618.8 14.913.5 30.227.6 8.4 9.1 7.7 7.0 16.414.7 19.517.2127.0 155.0 18.716.3 9.5 9.5;ODS HTML FILE=D: RESULT2.XLS;PROC univariate DATA=c.t2 ;var d ; RUN;ODS HTML CLOSE;两独立样本的秩和检验（NPAR1WAY+ wilcoxon）data c.t3;input group motality;datalines;1 32.52 16.0 1 35.52 22.51 40.52 26.0 1 40.52 28.51 49.02 32.5 1 49.52 38.01 51.52 40.5;/*建立