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# mdsprox

Class: TreeBagger

Multidimensional scaling of proximity matrix

## Syntax

```[S,E] = mdsprox(B) [S,E] = mdsprox(B,'param1',val1,'param2',val2,...) ```

## Description

`[S,E] = mdsprox(B)` returns scaled coordinates, `S`, and eigenvalues, `E`, for the proximity matrix in the ensemble `B`. An earlier call to `fillprox(B)` must create the proximity matrix.

`[S,E] = mdsprox(B,'param1',val1,'param2',val2,...)` specifies optional parameter name/value pairs:

 `'Keep'` Array of indices of observations in the training data to use for multidimensional scaling. By default, this argument is set to `'all'`. If you provide numeric or logical indices, the method uses only the subset of the training data specified by these indices to compute the scaled coordinates and eigenvalues. `'Colors'` If you supply this argument, `mdsprox` makes overlaid scatter plots of two scaled coordinates using specified colors for different classes. You must supply the colors as a character vector or a string scalar with one letter for each color. If there are more classes in the data than letters in the supplied value, `mdsprox` plots only the first `C` classes, where `C` is the number of letters in the supplied value. For regression, the method uses the first color for all observations in `X`. `'MDSCoordinates'` Indices of the two scaled coordinates to plot. By default, `mdsprox` makes a scatter plot of the first and second scaled coordinates which correspond to the two largest eigenvalues. You can specify any other two or three indices not exceeding the dimensionality of the scaled data. This argument has no effect unless you also supply the `'Colors'` argument.

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