Missing Values in SAS

In SAS, Numeric and Character missing values are represented differently.

Numeric Missing Values

SAS stores 28 missing values in a numeric variable. They are as follows :

  1. dot-underscore  . _ 
  2. dot .
  3. .A through .Z ( Not case sensitive)
Sorting Order : dot- underscore is the lowest valued missing value. After the dot-underscore, comes the dot, and then the dot-A. The dot-Z is the highest valued missing value.

Run the following code and see how SAS treats them missing value

data temp;
input x;
cards;
1
2
3
.
.A
.X
.Z
._
4
;
run;

proc freq;
table x;
run; 


Check for missing numeric values

The following code checks for dot missing value only. It does not check for other 27 special numeric missing values (._ , .A through .Z)
If x =. then PUT "x is missing";
The following code checks for all 28 numeric missing values (.  , ._ , .A through .Z)
If x <=.z then PUT "x is missing";
The MISSING function accepts either a character or numeric variable as the argument and returns the value 1 if the argument contains a missing value or zero otherwise.
If missing(x) then PUT "x is missing";

Character missing values

Character missing values are represented by a single blank enclosed in quotes ' '.
If y = ' ' then put "y is missing";
If missing(y) then put "y is missing";

Working with Missing Values

Suppose we have a data set containing three variables - X, Y and Z. They all have some missing values. We wish to compute sum of all the variables.


data mydata2;
set mydata;
a=sum(x,y,z);
p=x+y+z;
run;
SUM function returns the sum of non-missing arguments whereas “+” operator returns a missing value if any of the arguments are missing.


Functions that handle MISSING data

NMISS : The NMISS() function will return the number of missing values in the specified list of numeric variables. The NMISS() function will convert any character values to numeric before assessing if the argument value is missing. 
IF NMISS(x,y,z) = 0 then PUT " All variables have non-missing values";
CMISS : The CMISS() function introduced in SAS 9.2 is similar to the NMISS() function that it counts the number arguments that are missing, but for both character and numeric variables without requiring character values to be converted to numeric.

N :  The N() function returns the number of non-missing values in a list of numeric variables.

CALL MISSING

SAS provides the statement CALL MISSING() to explicitly initialise or set a variable value to be missing.
data _null_;
call missing( num_1, num_2, x ); num_3 = x;
put num_1 = / num_2 = / num_3 = ;
run; 

Delete empty rows
options missing = ' ';
data readin;
set outdata;
if missing(cats(of _all_)) then delete;
run;

How missing values are handled in SAS procedures

 1.  PROC FREQ


By default, missing values are excluded and percentages are based on the number of non-missing values. If you use the "/ MISSING" option on the tables statement, the percentages are based on the total number of observations (non-missing and missing) and the percentage of missing values are reported in the table.
PROC FREQ DATA= TEST;
TABLES X / MISSING;
RUN;
 2.  PROC MEANS

It produces statistics on non-missing data only. The NMISS option is used to calculate number of missing values.
Proc Means Data = test N NMISS;
Var q1 - q5 ;
Run;
To see number of observations having a missing value for the classification variable, type MISSING option in PROC MEANS.
Proc Means data = test N NMISS MISSING;
Class Age ;
Var q1 - q5;
Run;
3. PROC CORR

By default, correlations are computed based on the number of pairs with non-missing data (pairwise deletion of missing data). The nomiss option can be used on the proc corr statement to request that correlations be computed only for observations that have non-missing data for all variables on the var statement (listwise deletion of missing data).

4. PROC REG

If any of the variables on the model or var statement are missing, they are excluded from the analysis (i.e., listwise deletion of missing data)

5. PROC LOGISTIC

If any of the variables on the model or var statement are missing, they are excluded from the analysis (i.e., listwise deletion of missing data)

6. PROC FACTOR

Missing values are deleted listwise, i.e., observations with missing values on any of the variables in the analysis are omitted from the analysis.

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