Given a matrix, and a distance measure, an embedding of the rows into desired Euclidean space is performed using non-Metric Multi-Dimensional Scaling.
1 |
data |
matrix whose rows shall be embedded. |
embed.dim |
Dimensionality of Euclidean space into which embedding shall be performed. |
n.iters |
Number of iterations of the nMDS scheme |
metric |
The distance metric used to compare rows. Currently only "pearson" and "euclidean" ae supported. |
random.seed |
A random seed used by nMDS. Use of this option allows reproducability of nMDS results |
non-Metric Multi-Dimensional Scaling is performed using the scheme proposed by Taguchi and Oono.
If an element is missing (NA) in a particular row, all distance comparisons to that row shall ignore that particular element.
An object of class "nMDS" containing:
x |
matrix with the same number of rows and row names as |
Satwik Rajaram and Yoshi Oono
Relational patterns of gene expression via non-metric multidimensional scaling analysis: Y.-h. Taguchi and Y. Oono, Bioinformatics, 2005 21(6):730-740.
prcomp
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