If a value is higher than the 1.5 times of Interquartile Range (IQR) above the upper quartile (Q3), the value will be considered as **mild-outlier**. Similarly, if a value is lower than the 1.5 times of IQR below the lower quartile (Q1), the value will be considered as **mild-outlier**.

If a value is higher than the 3 times of Interquartile Range (IQR) above the upper quartile (Q3), the value will be considered as **extreme-outlier**. Similarly, if a value is lower than the 3 times of IQR below the lower quartile (Q1), the value will be considered as **extreme-outlier**.

IQR is interquartile range. It measures dispersion or variation. IQR = Q3 -Q1.

Lower limit of acceptable range = Q1 - 3* (Q3-Q1)

Upper limit of acceptable range = Q3 + 3* (Q3-Q1)

The following macro calculates the lower and upper limit values of acceptable range and removes the observations that are outside this range. It works for **multiple variables**.

Updated - June17, 2016 - I have modified the code. The code works for **removing outliers for multiple variables. **For example, there are two continuous variables having extreme values. It will remove observations wherein extreme values exist. It can be same rows for both the variables or difference rows having extreme values for them.

%macro outliers(input=, var=, output= ); %let Q1=; %let Q3=; %let varL=; %let varH=; %let n=%sysfunc(countw(&var)); %do i= 1 %to &n; %let val = %scan(&var,&i); %let Q1 = &Q1 &val._P25; %let Q3 = &Q3 &val._P75; %let varL = &varL &val.L; %let varH = &varH &val.H; %end; /* Calculate the quartiles and inter-quartile range using proc univariate */ proc means data=&input nway noprint; var &var; output out=temp P25= P75= / autoname; run; /* Extract the upper and lower limits into macro variables */ data temp; set temp; ID = 1; array varb(&n) &Q1; array varc(&n) &Q3; array lower(&n) &varL; array upper(&n) &varH; do i = 1 to dim(varb); lower(i) = varb(i) - 3 * (varc(i) - varb(i)); upper(i) = varc(i) + 3 * (varc(i) - varb(i)); end; drop i _type_ _freq_; run; data temp1; set &input; ID = 1; run; data &output; merge temp1 temp; by ID; array var(&n) &var; array lower(&n) &varL; array upper(&n) &varH; do i = 1 to dim(var); if not missing(var(i)) then do; if var(i) >= lower(i) and var(i) <= upper(i); end; end; drop &Q1 &Q3 &varL &varH ID i; run; %mend; %outliers(input=tt, var= age weight height, output= outresult);

*If you want to cap extreme values rather than removing them, you can check out the following link :*

**SAS Macro : Capping Outliers**

**Multiple ways to detect and solve problem of outliers**

Thank you for this!

ReplyDeletevery good!

ReplyDeleteWhat does Range2 in the last line denote? Plz reply..only error is coming there..

ReplyDeleteDude... That is how you have to run your function... If you are putting multiple variables than you have to give variable range & if only 1 variable then just give the variable name for which you are removing outliers.

ReplyDeletehi Atul,

ReplyDeleteI've tried to modify your code and run it for multiple variables and it hasn't worked for me. Could you post an example of doing this with more than one variable?

I was wondering whether I need to introduce the scan(), to iterate through various vars, etc?

Thanks.. this is greatly appreciated. Makes me realize I dont understand SAS Macros.

Specifically what I did was create a macro variable in using the :varlist in proc sql, then I reference that &varlist when calling the outliers macro, i.e., %outliers(Input=test, Var = &varlist, Output= Outdata);

ReplyDeletethat doesn't work :(

Check out this version of macro. It works for multiple variables - http://www.listendata.com/2015/04/sas-macro-capping-outliers.html

DeleteThanks, I'll take a look. You seem to have a lot more stuff on your blog. Great job :)

DeleteCheers. I have modified the above code. Let me know if you face any issue(s). Thanks!

DeleteHi Deepanshu,

DeleteI am loving your blog. What advice would you give someone who is trying to learn advanced SAS Macros fast? I've been using SAS for years, but I've never really had to write complex programs because there was no need to. Now I work with huge data sets, and I'm starting to see the need to catch up. I have Carpenter's Complete Guide to the SAS Macro Language, which I try to read. What else could I do? I plan to read through your tutorials also.

An issues I have with this macro is that if you want to run it for 50 variables then you have to list all of them here?

ReplyDelete%outliers(input=tt, var= age weight height, output= outresult);

That works but it is not as elegant!!

DeleteSays someone who can't come up with anything close to this :)

An issue i have with your comment is you are posting as anonymous. I don't want to encourage readers to post comment as anonymous especially when they are seeking help. Thanks!

Deletecould you please explain how to remove outliers, I mean theoretical. How to identify and method I have understood,but how to remove I want to know.

ReplyDeleteIt thought should always be 1.5 times IQR and not 3 times

ReplyDeleteAs you have mentioned to calculate quartiles and interquartiles using proc univariate but you have used proc means. Please confirm if it univariate has to be used or proc means and do we have to modify something in the code?

ReplyDeleteWorked like a charm, I really appreciate you sharing it.

ReplyDelete