t-Distributed Stochastic Neighbor Embedding
This function is a wrapper for the Rtsne function in the Rtsne package by Krijthe and van der Maaten. The purpose is to convert the output to class ‘dsvord’ to simplify plotting and additional graphical analysis as well as to provide a summary method.
tsne(dis,k=2,perplexity=30,theta= 0.0,eta=200) besttsne(dis,k=2,itr=100,perplexity=30,theta=0.0,eta = 200)
dis |
a dist object returned from |
k |
the desired number of dimensions for the result |
perplexity |
neighborhood size parameter (should be less than (size(dis)-1) /3 |
theta |
Speed/accuracy trade-off; set to 0.0 for exact TSNE, (0,0,0.5] for increasing speeed (default: 0.0) |
eta |
Learning rate |
itr |
number of random starts to find best result |
The tsne function simply calls the Rtsne function of the
Rtsne package with a specified distance/dissimilarity matrix rather
than the community matrix. By convention, t-SNE employs a PCA on the
input data matrix, and calculates distances among the first 50 eigenvectors
of the PCA. Rtsne, however, allows the submission of a pre-calculated
distance/dissimilarity matrix in place of the PCA. Given the long history of
research into the use of PCA in ecological community analysis, tsne allows
the simple use of any of a vast number of distance/dissimilarity matrices
known to work better with ecological data.
In addition, the tsne function converts the output to an object of class
‘dsvord’ to simplify plotting and analyses using the many functions
defined for objects of class ‘dsvord’. (see
plot.dsvord for more details.)
The ‘besttsne’ function runs one run from a PCO solution as the initial configuration and ‘itr-1’ number of random initial locations and returns the best result of the set.
an object of class ‘dsvord’, with components:
points |
the coordinates of samples along axes |
type |
‘t-SNE’ |
tsne is included as part of the LabDSV package to provide a consistent interface and utility for ecological community ordination methods. Other analyses included with the same interface at present include nonmetric multidimensional scaling (NMDS), principal components analysis (pca), and principal coordinates analysis (pco).
Jesse H. Krijthe for the original Rtsne R code, adapted from C++ code from Laurens van der Maaten.
David W. Roberts droberts@montana.edu http://ecology.msu.montana.edu/droberts/droberts.html for the adaptation to the LabDSV protocol.
van der Maaten, L. 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245.
van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605.
Krijthe, J,H, 2015. Rtsne: T-Distributed Stochastic Neighbor Embedding using a Barnes-Hut Implementation, URL: https://github.com/jkrijthe/Rtsne
Rtsne for the original function
plot.dsvord for the ‘plot’ method, the ‘plotid’
method to identify points with a mouse, the ‘points’ method to
identify points meeting a logical condition, the ‘hilight’ method
to color-code points according to a factor,
the ‘chullord’ method to add convex hulls for a factor, or the
the ‘surf’ method to add surface contours for
continuous variables.
data(bryceveg) data(brycesite) dis.man <- dist(bryceveg,method="manhattan") demo.tsne <- tsne(dis.man,k=2) plot(demo.tsne) points(demo.tsne,brycesite$elev>8000) plotid(demo.tsne,ids=row.names(brycesite))
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