Computes multidimensional scaling and displays the output as a twodimensional scatterplot.
Technical details
Description
Metric MDS minimizes the difference between distances in input and output spaces. Nonmetric MDS aims to preserve the ranking of distances between input and output spaces. You can find out more about MDS on our blog here.
Inputs
Algorithm A choice between metric and nonmetric multidimensional scaling. Other dimension reduction techniques of PCA and tSNE are also available.
The input data can be provided via one of three options:

 Variables The variables or a questionvariable set containing variables that you would like to analyze. Cases with missing data are ignored.
 Distance matrix Select an existing distance matrix. This should be a symmetric matrix of distances, such as the output of Correlation  Distances.
 Paste or type distance matrix Opens up a blank spreadsheet into which tabular data can be manually entered or pasted.
Group variable A variable to categorize the output. If numeric, the data are shaded from light (lowest values) to dark (highest). If categorical, data points are colored according to their category. This option is only available if Variables are provided.
Create binary variables from unordered categories If selected, unordered categorical Variables with N categories are converted are converted into N1 binary indicator variables. Otherwise such variables are each converted to a single numeric variable with integers representing categories (as happens for ordered categories). This option is only available if Variables are provided.
Normalize variables For Variables input, whether to normalize the data.

 For tSNE and MDS each variable is standardized to the range [0, 1].
 For PCA the correlation matrix is used rather than the covariance matrix.
Perplexity A parameter used by the tSNE algorithm and related to the number of nearest neighbors considered when placing each data point. The typical useful range is from 5 to 50.

 Low values imply that immediately local structure is most important.
 High values increase the impact of more distant neighbors and global structure.
Output
Using Variables
If the input type is Variables, the probability that each point has the same class as its nearest neighbor is calculated. A further variable may be specified to classify the output cases into groups using the Group variable field.
Using Metric
Using Nonmetric
Using a Distance Matrix
Using Metric
Using Nonmetric
Input example for distance matrix pasted in:
toast  butoast  engmuff  jdonut  cintoast  bluemuff  hrolls  toastmarm  butoastj  toastmarg  cinbun  danpastry  gdonut  cofcake  
butoast  15  
engmuff  25  15  
jdonut  3  24  22  
cintoast  14  3  17  22  
bluemuff  24  17  2  21  19  
hrolls  28  8  4  27  18  8  
toastmarm  7  7  20  11  6  18  23  
butoastj  8  6  21  12  5  19  22  2  
toastmarg  16  2  16  25  4  18  9  8  7  
cinbun  26  17  10  17  12  7  18  20  19  18  
danpastry  21  25  11  5  19  10  22  17  16  26  2  
gdonut  20  18  24  2  23  22  25  11  12  17  4  11  
cofcake  16  22  11  13  21  7  21  21  20  23  6  7  11  
cornmuff  27  11  3  26  16  4  5  25  24  12  12  16  24  16 
Additional Properties
When using this feature you can obtain additional information that is stored by inspecting it using custom R code in an item below:
#change YourReferenceName to the reference name (under Properties > General) of your analysis
item = YourReferenceName
str(item)
Acknowledgements
Uses the R packages MASS and isoMDS.
References
Analyzing Multivariate Data, by J. Lattin, J.D. Carroll, and P.E. Green, Brooks/Cole, 2003.
Method
 In Displayr: How to Create a Dimension Reduction Scatterplot
 In Q: Create > Dimension Reduction > Multidimensional Scaling (MDS)