CALL STDIZE Routine

Standardizes the values of one or more variables.

Category:	Mathematical
Interaction:	When invoked by the %SYSCALL macro statement, CALL STDIZE removes the quotation marks from its arguments. For more information, see Invoking CALL Routines and the %SYSCALL Macro Statement.
Note:	Argument types for arguments that are updated must match in CALL routines. All argument types must be CHAR, VARCHAR, or NUMERIC. If the argument types do not match, a warning is issued to the log.

Table of Contents

Syntax

CALL STDIZE(<option-1, option-2, …, option-n> variable-1 <,variable-2, …, variable-n>);

Required Argument

variable

is numeric. These values are standardized according to the method that you use.

Optional Arguments

option

specifies a character expression whose values can be uppercase, lowercase, or mixed-case letters. Leading and trailing blanks are ignored. option includes the following three categories:

Restriction	Use a separate argument for each option because you cannot specify more than one option in a single argument.
Tip	Character expressions can end with an equal sign that is followed by another argument that is a numeric constant, variable, or expression.
See	PROC STDIZE in SAS/STAT 9.3 User’s Guide for information about formulas and other details. The options that are used in CALL STDIZE are the same as those used in PROC STDIZE.

standardization-options

specifies how to compute the location and scale measures that are used to standardize the variables. The following standardization options are available:

ABW=	must be followed by an argument that is a numeric expression that specifies the tuning constant.
AGK=	must be followed by an argument that is a numeric expression that specifies the proportion of pairs to be included in the estimation of the within-cluster variances.
AHUBER=	must be followed by an argument that is a numeric expression that specifies the tuning constant.
AWAVE=	must be followed by an argument that is a numeric expression that specifies the tuning constant.
EUCLEN	specifies the Euclidean length.
IQR	specifies the interquartile range.
L=	must be followed by an argument that is a numeric expression with a value greater than or equal to 1 that specifies the power to which differences are to be raised in computing an L(p) or Minkowski metric.
MAD	specifies the median absolute deviation from the median.
MAXABS	specifies the maximum absolute values.
MEAN	specifies the arithmetic mean (average).
MEDIAN	specifies the middle number in a set of data that is ordered according to rank.
MIDRANGE	specifies the midpoint of the range.
RANGE	specifies a range of values.
SPACING=	must be followed by an argument that is a numeric expression that specifies the proportion of data to be contained in the spacing.
STD	specifies the standard deviation.
SUM	specifies the result that you obtain when you add numbers.
USTD	specifies the standard deviation about the origin, based on the uncorrected sum of squares.

VARDEF-options

specifies the divisor to be used in the calculation of variances. VARDEF options can have the following values:

DF	specifies degrees of freedom.
N	specifies the number of observations. The default is DF.

miscellaneous-options

Miscellaneous options can have the following values:

ADD=	is followed by a numeric argument that specifies a number to add to each value after standardizing and multiplying by the value from the MULT= option. The default value is 0.
FUZZ=	is followed by a numeric argument that specifies the relative fuzz factor.
MISSING=	is followed by a numeric argument that specifies a value to be assigned to variables that have a missing value.
MULT=	is followed by a numeric argument that specifies a number by which to multiply each value after standardizing. The default value is 1.
NORM	normalizes the scale estimator to be consistent for the standard deviation of a normal distribution. This option affects only the methods AGK=, IQR, MAD, and SPACING=.
PSTAT	writes the values of the location and scale measures in the log.
REPLACE	replaces missing values with the value 0 in the standardized data. (This value corresponds to the location measure before standardizing.) To replace missing values by other values, see the MISSING= option.
SNORM	normalizes the scale estimator to have an expectation of approximately 1 for a standard normal distribution. This option affects only the SPACING= method.

Details

The CALL STDIZE routine transforms one or more arguments that are numeric variables by subtracting a location measure and dividing by a scale measure. You can use a variety of location and scale measures. The default location option is MEAN, and the default scale option is STD.

In addition, you can multiply each standardized value by a constant, and you can add a constant. Here is the final output value: $table with 1 row and 1 column , row1 column 1 , r e s u l t equals eh d d plus m u l t times . open . fraction open o r i g i n eh l minus l o c eh t i o n close , over s c eh l e end fraction . close , end table. Click image for alternative formats.$ .

These are the descriptions of the variables:

result: specifies the final value that is returned for each variable.
add: specifies the constant to add (ADD= option).
mult: specifies the constant to multiply by (MULT= option).
original: specifies the original input value.
location: specifies the location measure.
scale: specifies the scale measure.

You can replace missing values by any constant. If you do not specify the MISSING= option or the REPLACE option, variables that have missing values are not altered. The initial estimation method for the ABW=, AHUBER=, and AWAVE= methods is MAD. Percentiles are computed using definition 5. For more information about percentile calculations, see SAS Elementary Statistics Procedures in Base SAS Procedures Guide.

Comparisons

The CALL STDIZE routine is similar to the STDIZE procedure in the SAS/STAT product. However, the CALL STDIZE routine is primarily useful for standardizing the rows of a SAS data set, whereas the STDIZE procedure can standardize only the columns of a SAS data set. For more information, see PROC STDIZE in SAS/STAT User's Guide.

Example

data _null_;
   retain x 1 y 2 z 3;
   call stdize(x,y,z);
   put x= y= z=;
run;

data _null_;
   retain x 1 y 2 z 3;
   call stdize(x,y,z);
   put x= y= z=;
run;

The preceding statements produce these results:

x=-1 y=0 z=1

data _null_;
   retain w 10 x 11 y 12 z 13;
   call stdize('iqr',w,x,y,z);
   put w= x= y= z=;
run;

data _null_;
   retain w 10 x 11 y 12 z 13;
   call stdize('iqr',w,x,y,z);
   put w= x= y= z=;
run;

The preceding statements produce these results:

w=-0.75 x=-0.25 y=0.25 z=0.75

data _null_;
   retain w . x 1 y 2 z 3;
   call stdize('range',w,x,y,z);
   put w= x= y= z=;
run;

data _null_;
   retain w . x 1 y 2 z 3;
   call stdize('range',w,x,y,z);
   put w= x= y= z=;
run;

The preceding statements produce these results:

w=. x=0 y=0.5 z=1

data _null_;
      retain w . x 1 y 2 z 3;
      call stdize('mult=',10,'missing=',-1,'range',w,x,y,z);
      put w= x= y= z=;
   run;

data _null_;
      retain w . x 1 y 2 z 3;
      call stdize('mult=',10,'missing=',-1,'range',w,x,y,z);
      put w= x= y= z=;
   run;

The preceding statements produce these results:

w=-1 x=0 y=5 z=10

Last updated: April 14, 2026