sas求缺失值的三中方法

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方法一:（利用数组求缺失值）

data quan(keep= k);

set quan.cydc;

array x _numeric_;

  do i=1 to dim(x);

   if x(i)=. then j+1;

   end;

   if _n_>1 then k=dif1(j);

   run;

proc print data=quan;

run;

方法二:（转置方法用过程步求缺失值）

proc transpose data=quan.BAIPU out=quan;

var X1-X10;

run;

proc means data=quan nmiss;

  var COL1-COL10;

  output out=quan1nmiss=miss1;

  run;

方法三：（转置后用sql求缺失值）

proc transpose data=quan.BAIPU out=quan;

var X1-X10;

run;

proc sql;

  select nmiss(col1)as miss1,nmiss(col2)asmiss2

  from quan;

  quit;

这些都是比较简单的程序，希望大牛们跟帖高级程序。

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关键词：缺失值 Transpose proc sql numeric output sql

how about using functions (NMISS & CMISS) directly?

It is always better to use existing SAS functions. See  the example below

data t1;
    array p(*) p1-p10;
   do i=1 to 5;
      do j=1 to dim(p);
            if ranuni(123)>=0.5 then p(j)=.;
                else p(j)=1;
          end;
        output;
        end;
        drop i j;
run;

data t2;
  set t1;
  array x _numeric_;
  n_miss=nmiss(of x(*));
run;

proc print;run;

bobguy 发表于 2013-1-19 07:54
It is always better to use existing SAS functions. See  the example below

data t1;

表示长见识了

Contrast to traditional way to delete missing values in the model, some procedures in sas start a new algorithm called Full Information Maximum Likelihood to take advantage of residual information after missing. in this case, sas will describe how the missing values are distributed in the data, which is much more advanced than we do it manually as above. for examples, the SEM model in SAS.
JingJu

学习了

不错的分析啊

假设num表示数字变量的空值总数，char表示字符变量的空值总数,all表示总空值数
num+cmiss(of _numeric_);
char+cmiss(of _character_);all+cmiss(of _all_);
最后一行就是总数了
如果想单看每行的空值数，请把加号改成等号。

好好好