Standardization Method for Predictor Continuous Variables

Use the drop-down menu to specify a method for standardizing continuous predictor variables.

Standardization is helpful when you want to ensure that all of the continuous predictor variables have the same locations and/or scales.

Note: When you specify a Test Data Set or when you are running this process through Cross Validation Model Selection, standardization is performed only on the training data. The resulting location and scale statistics are used to standardize the test data.

Available methods for standardization are described in the table below:

Method

Description

STD

Standardizes values to the mean with a scale equivalent to the standard deviation.

MEAN

Standardizes values to the mean with a scale equivalent to one (1).

MEDIAN

Standardizes values to the median with a scale equivalent to one (1).

SUM

Standardizes values to zero (0) with a scale equivalent to the sum of the values.

EUCLEN

Standardizes values to zero (0) with a scale equivalent to the Euclidean length.

USTD

Standardizes values to zero (0) with a scale equivalent to the standard deviation about the origin.

RANGE

Standardizes values to the minimal value with a scale equivalent to the range of the values.

MIDRANGE

Standardizes values to the midrange value with a scale equivalent to the range of the values divided by two (2).

MAXABS

Standardizes values to zero (0) with a scale equivalent to the maximum absolute value.

IQR

Standardizes values to the median with a scale equivalent to the interquartile range.

MAD

Standardizes values to the median with a scale equivalent to the mean absolute deviation from the median.

ABW(4.685)

Standardizes values to the biweight 1-step M-estimate with a scale equivalent to the biweight A-estimate.

Note: 4.685 is the default numeric tuning constant used in this method.

AHUBER(1.345)

Standardizes values to the Huber 1-step M-estimate with a scale equivalent to the Huber A-estimate.

Note: 1.345 is the default numeric tuning constant used in this method.

AWAVE(1)

Standardizes values to the mean with a scale equivalent to the Wave A-estimate.

Note: 1 is the default numeric tuning constant used in this method.

AGK(0.1)

Standardizes values to the mean with a scale equivalent to the AGK estimate.

Note: 0.1 is the default numeric constant giving the proportion of pairs to be included in the estimation of the within-cluster variances.

SPACING(0.1)

Standardizes values to the mid-minimum spacing with a scale equivalent to the minimum spacing.

Note: 0.1 is the default numeric constant giving the proportion of data to be contained in the spacing.

L(1)

Standardizes values to the L(1) value with a scale equivalent to the L(1) value.

Note: 0.1 is the default numeric constant specifying the power to which differences are to be raised in computing an L(p) or Minkowski metric.

To Specify a Standardization Method:

8 Select the desired standardization method using the drop-down menu.

Note: Select the blank space to apply no standardization to your variables.

For Additional Information

Refer to the SAS PROC STDIZE documentation for more information.