labdsv (version 2.1-0)

tsne: t-Distributed Stochastic Neighbor Embedding

Description

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.

Usage

tsne(dis,k=2,perplexity=30,theta= 0.0,eta=200)
besttsne(dis,k=2,itr=100,perplexity=30,theta=0.0,eta = 200,pbar=TRUE)

Value

an object of class ‘dsvord’, with components:

points

the coordinates of samples along axes

type

‘t-SNE’

Arguments

dis

a dist object returned from dist or a full symmetric dissimilarity or distance matrix

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

pbar

switch to control printing progress bar in interactive sessions

Author

Jesse H. Krijthe for the original Rtsne R code, adapted from C++ code from Laurens van der Maaten.

David W. Roberts

Details

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.

References

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

See Also

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.

Examples

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))