Description Usage Arguments Value Examples
This function is a simple wrapper of Rtsne
function for
t-Stochastic Neighbor Embedding for finding low-dimensional structure of
the data embedded in the high-dimensional space.
1 |
data |
an (n\times p) matrix whose rows are observations. |
ndim |
an integer-valued target dimension. |
... |
extra parameters to be used in |
a named list containing
an (n\times ndim) matrix whose rows are embedded observations.
discrepancy between embedded and origianl data as a measure of error.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ## use simple example of iris dataset
data(iris)
mydat = as.matrix(iris[,1:4])
mylab = as.factor(iris[,5])
## run t-SNE and MDS for comparison
iris.cmds = cmds(mydat, ndim=2)
iris.tsne = tsne(mydat, ndim=2)
## extract coordinates and class information
cx = iris.cmds$embed # embedded coordinates of CMDS
tx = iris.tsne$embed # t-SNE
## visualize
# main title
mc = paste("CMDS with STRESS=",round(iris.cmds$stress,4),sep="")
mt = paste("tSNE with STRESS=",round(iris.tsne$stress,4),sep="")
# draw a figure
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(cx, col=mylab, pch=19, main=mc)
plot(tx, col=mylab, pch=19, main=mt)
par(opar)
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