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Nonclassical multidimensional scaling


Y = mdscale(D,p)
[Y,stress] = mdscale(D,p)
[Y,stress,disparities] = mdscale(D,p)
[...] = mdscale(D,p,'Name',value)


Y = mdscale(D,p) performs nonmetric multidimensional scaling on the n-by-n dissimilarity matrix D, and returns Y, a configuration of n points (rows) in p dimensions (columns). The Euclidean distances between points in Y approximate a monotonic transformation of the corresponding dissimilarities in D. By default, mdscale uses Kruskal's normalized stress1 criterion.

You can specify D as either a full n-by-n matrix, or in upper triangle form such as is output by pdist. A full dissimilarity matrix must be real and symmetric, and have zeros along the diagonal and non-negative elements everywhere else. A dissimilarity matrix in upper triangle form must have real, non-negative entries. mdscale treats NaNs in D as missing values, and ignores those elements. Inf is not accepted.

You can also specify D as a full similarity matrix, with ones along the diagonal and all other elements less than one. mdscale transforms a similarity matrix to a dissimilarity matrix in such a way that distances between the points returned in Y approximate sqrt(1-D). To use a different transformation, transform the similarities prior to calling mdscale.

[Y,stress] = mdscale(D,p) returns the minimized stress, i.e., the stress evaluated at Y.

[Y,stress,disparities] = mdscale(D,p) returns the disparities, that is, the monotonic transformation of the dissimilarities D.

[...] = mdscale(D,p,'Name',value) specifies one or more optional parameter name/value pairs that control further details of mdscale. Specify Name in single quotes. Available parameters are

  • Criterion— The goodness-of-fit criterion to minimize. This also determines the type of scaling, either non-metric or metric, that mdscale performs. Choices for non-metric scaling are:

    • 'stress' — Stress normalized by the sum of squares of the inter-point distances, also known as stress1. This is the default.

    • 'sstress' — Squared stress, normalized with the sum of 4th powers of the inter-point distances.

    Choices for metric scaling are:

    • 'metricstress' — Stress, normalized with the sum of squares of the dissimilarities.

    • 'metricsstress' — Squared stress, normalized with the sum of 4th powers of the dissimilarities.

    • 'sammon' — Sammon's nonlinear mapping criterion. Off-diagonal dissimilarities must be strictly positive with this criterion.

    • 'strain' — A criterion equivalent to that used in classical multidimensional scaling.

  • Weights — A matrix or vector the same size as D, containing nonnegative dissimilarity weights. You can use these to weight the contribution of the corresponding elements of D in computing and minimizing stress. Elements of D corresponding to zero weights are effectively ignored.

      Note:   When you specify weights as a full matrix, its diagonal elements are ignored and have no effect, since the corresponding diagonal elements of D do not enter into the stress calculation.

  • Start — Method used to choose the initial configuration of points for Y. The choices are

    • 'cmdscale' — Use the classical multidimensional scaling solution. This is the default. 'cmdscale' is not valid when there are zero weights.

    • 'random' — Choose locations randomly from an appropriately scaled p-dimensional normal distribution with uncorrelated coordinates.

    • An n-by-p matrix of initial locations, where n is the size of the matrix D and p is the number of columns of the output matrix Y. In this case, you can pass in [] for p and mdscale infers p from the second dimension of the matrix. You can also supply a 3-D array, implying a value for 'Replicates' from the array's third dimension.

  • Replicates — Number of times to repeat the scaling, each with a new initial configuration. The default is 1.

  • Options — Options for the iterative algorithm used to minimize the fitting criterion. Pass in an options structure created by statset. For example,

    opts = statset(param1,val1,param2,val2, ...);
    [...] = mdscale(...,'Options',opts)

    The choices of statset parameters are

    • 'Display' — Level of display output. The choices are 'off' (the default), 'iter', and 'final'.

    • 'MaxIter' — Maximum number of iterations allowed. The default is 200.

    • 'TolFun' — Termination tolerance for the stress criterion and its gradient. The default is 1e-4.

    • 'TolX'— Termination tolerance for the configuration location step size. The default is 1e-4.


load cereal.mat
X = [Calories Protein Fat Sodium Fiber ...
     Carbo Sugars Shelf Potass Vitamins];

% Take a subset from a single manufacturer.
X = X(strcmp('K',cellstr(Mfg)),:);

% Create a dissimilarity matrix.
dissimilarities = pdist(X);
% Use non-metric scaling to recreate the data in 2D,
% and make a Shepard plot of the results.
[Y,stress,disparities] = mdscale(dissimilarities,2);
distances = pdist(Y);
[dum,ord] = sortrows([disparities(:) dissimilarities(:)]);
plot(dissimilarities,distances,'bo', ...
xlabel('Dissimilarities'); ylabel('Distances/Disparities')
legend({'Distances' 'Disparities'},'Location','NW');

% Do metric scaling on the same dissimilarities.
[Y,stress] = ... 
distances = pdist(Y);
plot(dissimilarities,distances,'bo', ...
[0 max(dissimilarities)],[0 max(dissimilarities)],'r.-');
xlabel('Dissimilarities'); ylabel('Distances')

See Also

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Introduced before R2006a

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