SAS Proc单变量和R分位数函数的不同结果

Question

我在R中使用函数分位数来计算第90,75,50,25百分位，但是我的同事使用SAS proc单变量进行相同的计算，我们得到了截然不同的结果（例如，来自R的第90百分位数）是47.36，但SAS的90％是50.64）。我试图找出原因。有人可以给我一些指导吗？

R代码：

分位数（c（43.55,41.30,39.40,40.93,38.74,39.97,45.38,41.48,45.01,42.03,44.71,43.42,45.83,43.44,37.84,50.64,53.16,45.95），prob = c（0.90， 0.10,0.75,0.50,0.25））

SAS代码：

    data x;
    input x;
    datalines;
    43.55
    41.30
    39.40
    40.93
    38.74
    39.97
    45.38
    41.48
    45.01
    42.03
    44.71
    43.42
    45.83
    43.44
    37.84
    50.64
    53.16
    45.95

    ;
    run;
    proc univariate data=x noprint ;
    var x;
    output out=new  p90=p90 p10=p10 q3=p75 median=p50 q1=p25 ;
    run;

Answer 1

R中的默认方法为7，而SAS默认方法可能是 empirical distribution function with averaging 。

如果您在R中使用添加选项type = 1，您将获得与SAS相同的结果。

quantile(c(43.55,41.30,39.40,40.93,38.74,39.97,45.38,41.48,45.01,
           42.03,44.71,43.42,45.83,43.44,37.84,50.64,53.16,45.95),
         prob=c(0.90, 0.10, 0.75, 0.50, 0.25),
         type = 1)
  90%   10%   75%   50%   25% 
50.64 38.74 45.38 43.42 40.93