Transformations Procedures

DISTANCE — Computes various measures of distance, dissimilarity, or similarity between the observations (rows) of a SAS data set. Proximity measures are stored as a lower triangular matrix or a square matrix in an output data set that can then be used as input to the CLUSTER, MDS, and MODECLUS procedures.
SCORE — Multiplies values from two SAS data sets, one containing coefficients and the other containing raw data to be scored using the coefficients from the first data set
SIMNORMAL — Performs conditional and unconditional simulation for a set of correlated normal random variables
STDIZE — Standardizes one or more numeric variables in a SAS data set by subtracting a location measure and dividing by a scale measure
TRANSREG — Fits linear models with optimal nonlinear transformations of variables

Transformations

Below are highlights of the capabilities of the SAS/STAT procedures that perform data transformations:

compute various measures of distance, dissimilarity, or similarity between the observations (rows) of a SAS data set
score data sets
standardize or center the variables from a raw data set
fit linear models with optimal nonlinear transformations of variables

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