Grubbs test

%macro  grubbs(dataset ,var ,id,alpha =0.05);
ods  select  none;
proc  means  data=&dataset  mean  var n;
var &var;
output  out=out  mean=mean  var=var n=n;
run;
data  outliers;
set &dataset(keep=&id &var);
if _n_=1 then  set  out;
/*  statistic  */u = abs((&var - mean) / sqrt(var));
/*  critical  value */t = quantile ("t", &alpha / (2*n), n-2);
c = (n-1) * sqrt(t**2 / (n * (t**2 + n - 2)));
/*  check if this is an  outlier  */if(u > c) then outlier = "yes";
else outlier = "no";
/* p-value */u_inv = u*sqrt((n-2)*n) / sqrt(1 - (u**2-(n-2))*n);
p_value = min (2*n*(1-cdf("t", u_inv , n-2)), 1);
keep &id  p_value u c outlier;
run;
proc  sort  data=outliers;
by  descending u;
run;
ods  select  all;
title1 "Grubbs  test";
title2 "Variable: &var";
proc  print  data=outliers(obs =1)  label  noobs;
var &id  p_value u c outlier;
label &id="id" p_value ="p-value" u=" statistic  (|u|)"c=" critical  value"outlier ="is  outlier ?";
run;
title;
%mend;

/* ================ EXAMPLE ============================*/
libname  SASDATA  "/folders/myfolders ";
data IVFTransformed;
    set SASDATA.IVF;
    TTP_transformed = SQRT(TTP);
run;

%grubbs(dataset=IVFTransformed,var=TTP_transformed,id=ID,alpha=0.05);

/* ================= CHECK FOR ASSUMPTIONS ============== */

/*If the data is normal distributed */
PROC UNIVARIATE DATA=IVF4monthTTP NORMAL;
VAR TTP_transformed;
PROBPLOT TTP_transformed /NORMAL(MU=est SIGMA=est);
RUN;