JMP 14.2 Online Documentation (English)
Discovering JMP
Using JMP
Basic Analysis
Essential Graphing
Profilers
Design of Experiments Guide
Fitting Linear Models
Predictive and Specialized Modeling
Multivariate Methods
Quality and Process Methods
Reliability and Survival Methods
Consumer Research
Scripting Guide
JSL Syntax Reference
JMP iPad Help
JMP Interactive HTML
Capabilities Index
JMP 13.2 Online Documentation
Multivariate Methods
•
Multidimensional Scaling
• Overview of the Multidimensional Scaling Platform
For the latest version of JMP Help, visit
JMP.com/help
.
Previous
•
Next
Overview of the Multidimensional Scaling Platform
The Multidimensional Scaling platform generates a plot of proximities among a set of objects. This plot can be used to visually explore structure in a data set. MDS is a multivariate technique that is used to visualize the patterns of proximities (distances, similarities) among a set of objects in a small number of dimensions. MDS is applied to a distance matrix. The coordinates for the MDS plot are obtained by minimizing a stress function (the difference between the actual and predicted proximities).
The term distance can refer to a measure of physical distance, such as between cities. More often distance is a subjective assessment rather than a precise measurement. Proximities can measure perceived similarities between brands of a product, correlations of crime rates, or economic similarities for a sample of countries. Distance can also be called proximity or similarity (dissimilarity). If the data are given as an attribute list, then a distance matrix is first constructed from the correlation structure of the attribute list.
For more information about multidimensional scaling, see Borg and Groenen (
2005
) or Jackson (
2003
).
Previous
•
Next
Help created on 3/19/2020