分类数据之logistic回归

/*分类变量分析之logistc（一），因变量为二分类变量数据coronary中ca为二分类因变量，sex、ecg为二分类自变量，所有的二分类变量用0、1进行区别，构成（0，1）矩阵*/data coronary;   input sex ecg ca count @@;   datalines;0 0 0 11 0 0 1 40 1 0 10 0 1 1 81 0 0 9 1 0 1 91 1 0 6 1 1 1 21;*scale选项用于对过度离散数据校正； descending,应变量ca按降序排序， sas中按y=1的概率建模，即ordered value为1对应y的取值 output语句设置输出结果,这里结果存在predict数据中，预测值为prob;proc logistic data=coronary descending;   freq count;   model ca=sex ecg / scale=none aggregate;   output out=predict pred=prob;run;proc print data=predict;run;*ods select:考虑交叉影响的参数估计剥离;ods select FitStatistics ParameterEstimates;proc logistic descending;   freq count;   model ca=sex ecg sex*ecg;run;*二、条件变量，class;*sentence是二分类因变量，type、prior为二分类字符型自变量;data sentence;   input type $ prior $ sentence $ count @@;   datalines;nrb some y 42 nrb some n 109nrb none y 17 nrb none n 75other some y 33 other some n 175other none y 53 other none n 359;*class：对分类变量进行0-1处理， ref= :设置参照水平，这里ref=first表示some作为参照水平 scale:指定离散参数估算方法，校正离散情况，给出“偏差和 Pearson 拟合优度统计量”  aggregate:设置皮尔逊卡方检验统计量;proc logistic data=sentence descending;   class type prior(ref=first) / param=ref;   freq count;   model sentence = type prior / scale=none aggregate;run;*拟合优度剥离;ods select GoodnessOfFit;proc logistic descending;   class type prior (ref=first) / param=ref;   freq count;   model sentence = type / scale=none aggregate=(type prior);run;*从sas结果中剥离分类水平、拟合优度、参数估计、似然比情况,单独显示;ods select ClassLevelInfo GoodnessOfFit            ParameterEstimates OddsRatios;proc logistic data=sentence descending;   class type prior(ref='none');   freq count;   model sentence = type prior / scale=none aggregate;run;*三、自变量是定性变量;data uti;   input diagnosis : $13. treatment $ response $ count @@;   datalines;complicated A cured 78 complicated A not 28complicated B cured 101 complicated B not 11complicated C cured 68 complicated C not 46uncomplicated A cured 40 uncomplicated A not 5uncomplicated B cured 54 uncomplicated B not 5uncomplicated C cured 34 uncomplicated C not 6;run;ods select FitStatistics;proc logistic;   freq count;   class diagnosis treatment /param=ref;   model response = diagnosis|treatment;run;ods select FitStatistics GoodnessOfFit           TypeIII OddsRatios;proc logistic;   freq count;   class diagnosis treatment;   model response = diagnosis treatment /   scale=none aggregate;run;*clodds：计算似然比的置信区间 clparm: 计算参数的置信区间;ods select ClparmPL CloddsPL;proc logistic;   freq count;   class diagnosis treatment;   model response = diagnosis treatment /   scale=none aggregate clodds=pl clparm=pl;run;*contrast:定制假设检验的方式，变量需要是矩阵形式;ods select ContrastTest ContrastEstimate;proc logistic;   freq count;   class diagnosis treatment /param=ref;   model response = diagnosis treatment;   contrast 'B versus A' treatment -1 1            / estimate=exp;   contrast 'A' treatment 1 0;   contrast 'joint test' treatment 1 0,                         treatment 0 1;run;*四、自变量连续有序的情况;data coronary;   input sex ecg age ca @@ ;   datalines;0 0 28 0 1 0 42 1 0 1 46 0 1 1 45 00 0 34 0 1 0 44 1 0 1 48 1 1 1 45 10 0 38 0 1 0 45 0 0 1 49 0 1 1 45 10 0 41 1 1 0 46 0 0 1 49 0 1 1 46 10 0 44 0 1 0 48 0 0 1 52 0 1 1 48 10 0 45 1 1 0 50 0 0 1 53 1 1 1 57 10 0 46 0 1 0 52 1 0 1 54 1 1 1 57 10 0 47 0 1 0 52 1 0 1 55 0 1 1 59 10 0 50 0 1 0 54 0 0 1 57 1 1 1 60 10 0 51 0 1 0 55 0 0 2 46 1 1 1 63 10 0 51 0 1 0 59 1 0 2 48 0 1 2 35 00 0 53 0 1 0 59 1 0 2 57 1 1 2 37 10 0 55 1 1 1 32 0 0 2 60 1 1 2 43 10 0 59 0 1 1 37 0 1 0 30 0 1 2 47 10 0 60 1 1 1 38 1 1 0 34 0 1 2 48 10 1 32 1 1 1 38 1 1 0 36 1 1 2 49 00 1 33 0 1 1 42 1 1 0 38 1 1 2 58 10 1 35 0 1 1 43 0 1 0 39 0 1 2 59 10 1 39 0 1 1 43 1 1 0 42 0 1 2 60 10 1 40 0 1 1 44 1;run;*拟合logistic模型 selection用于选择逐步回归方法,包括forward,backward,stepwise include:设定每个拟合模型中包含model语句中列的因子的个数 units :可以设置想要计算的似然比odds ratios;proc logistic data=coronary descending;   model ca=sex ecg age ecg*ecg age*age         sex*ecg sex*age ecg*age /         selection=forward include=3 details lackfit;run;proc logistic descending;   model ca=sex ecg age;   units age=10;run;*五、logistic回归诊断;data uti2;   input diagnosis : $13. treatment $ response trials;   datalines;complicated A 78 106complicated B 101 112complicated C 68 114uncomplicated A 40 45uncomplicated B 54 59uncomplicated C 34 40;*INFLUENCE诊断;proc logistic data=uti2;   class diagnosis treatment / param=ref;   model response/trials = diagnosis treatment/influence;run;proc logistic data=uti2;   class diagnosis treatment / param=ref;   model response/trials = diagnosis/scale=none                                      aggregate=(treatment diagnosis)                                      influence                                      iplots;run;*精确logistic回归方法,exact;data liver;   input time $ group $ status $ count @@;   datalines;early antidote severe 6 early antidote not 12early control severe 6 early control not 2delayed antidote severe 3 delayed antidote not 4delayed control severe 3 delayed control not 0late antidote severe 5 late antidote not 1late control severe 6 late control not 0;*estimate=both,表示对第一个exact语句中指定的变量进行精确点估计 joint,表示对第二个exact中time、group进行联合检验;proc logistic descending;   freq count;   class time(ref='early') group(ref='control') /param=ref;   model status = time group / scale=none aggregate clparm=wald;   exact 'Model 1' intercept time group / estimate=both;   exact 'Joint Test' time group / joint;run;