MDS Procedure
The MDS procedure fits two and threeway, metric and nonmetric multidimensional scaling models.
Multidimensional scaling refers to a class of methods. These methods estimate coordinates for a set of objects in a space of
specified dimensionality. The input data are measurements of distances between pairs of objects. A variety of models can be used that
include different ways of computing distances and various functions relating the distances to the actual data.
The following are highlights of the MDS procedure's features:
 estimates the following parameters by nonlinear least squares:
 configuration — the coordinates of each object in a Euclidean or weighted
Euclidean space of one or more dimensions
 dimension coefficients — for each data matrix, the coefficients that multiply each coordinate
of the common or group weighted Euclidean space to
yield the individual unweighted Euclidean space
 transformation parameters — intercept, slope, or exponent in a linear, affine, or power transformation
relating the distances to the data
 fits either a regression model of the form
fit(datum) = fit(trans(distance)) + error
or a measurement model of the form
fit(trans(datum)) = fit(distance) + error
where
 fit is a predetermined power or logarithmic transformation
 trans is an estimated (`optimal') linear, affine, power, or monotone transformation
 datum is a measure of the similarity or dissimilarity of two objects or stimuli
 distance is a distance computed from the estimated coordinates of the two objects and estimated
dimension coefficients in a space of one or more dimensions
 error is an error term assumed to have an approximately normal distribution and to be
independently and identically distributed for all data
 performs BY group processing, whcih enables you to obtain separate analyses on grouped observations
 performs weighted analysis
creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

For further details see the MDS Procedure
Examples