isomap {vegan} | R Documentation |
The function performs isometric feature mapping which consists of three simple steps: (1) retain only some of the shortest dissimilarities among objects, (2) estimate all dissimilarites as shortest path distances, and (3) perform metric scaling (Tenenbaum et al. 2000).
isomap(dist, ndim=10, ...) isomapdist(dist, epsilon, k, path = "shortest", fragmentedOK =FALSE, ...) ## S3 method for class 'isomap': summary(object, axes = 4, ...) ## S3 method for class 'isomap': plot(x, net = TRUE, n.col = "gray", ...) rgl.isomap(x, web = "white", ...)
dist |
Dissimilariies. |
ndim |
Number of axes in metric scaling (argument k in
cmdscale ). |
epsilon |
Shortest dissimilarity retained. |
k |
Number of shortest dissimilariteis retained for a point. If
both epsilon and k are given, epsilon will be used. |
path |
Method used in stepacross to estimate the
shortest path, with alternatives "shortest" and "extended" . |
fragmentedOK |
What to do if dissimilarity matrix is
fragmented. If TRUE , analyse the largest connected group,
otherwise stop with error. |
x, object |
An isomap result object. |
axes |
Number of axes displayed. |
net |
Draw the net of retained dissimilarities. |
n.col |
Colour of drawn net segments. |
web |
Colour of the web in rgl graphics. |
... |
Other parameters passed to functions. |
The function isomap
first calls function isomapdist
for
dissimilarity transformation, and then performs metric scaling for the
result. All arguments to isomap
are passed to
isomapdist
. The functions are separate so that the
isompadist
transformation could be easily used with other
functions than simple linear mapping of cmdscale
.
Function isomapdist
retains either dissimilarities equal or shorter to
epsilon
, or if epsilon
is not given, at least k
shortest dissimilarities for a point. Then a complete dissimilarity
matrix is reconstructed using stepacross
using either
flexible shortest paths or extended dissimilarities (for details, see
stepacross
).
De'ath (1999) actually published essentially the same method before
Tenenbaum et al. (2000), and De'ath's function is available in
xdiss
in package mvpart. The differences are that
isomap
introduced the k
criterion, whereas De'ath only
used epsilon
criterion. In practice, De'ath also retains
higher proportion of dissimilarities than typical isomap
.
In addition to the standard plot
function, function
rgl.isomap
can make dynamic 3D plots that can be rotated on the
screen. The functions is based on ordirgl
, but it adds
the connecting lines. The function passes extra arguments to
scores
and ordirgl
functions so that you
can select axes, or define colours and sizes of points.
Function isomapdist
returns a dissimilarity object similar to
dist
. Function isomap
returns an object of class
isomap
with plot
and summary
methods. The
plot
function returns invisibly an object of class
ordiplot
. Function scores
can extract
the ordination scores.
Tenenbaum et al. (2000) justify isomap
as a tool of unfolding a
manifold (e.g. a 'Swiss Roll'). Even with a manifold structure, the
sampling must be even and dense so
that dissimilarities along a manifold are shorter than across the
folds. If data do not have such a manifold structure, the results are
very sensitive to parameter values.
Jari Oksanen
De'ath, G. (1999) Extended dissimilarity: a method of robust estimation of ecological distances from high beta diversity data. Plant Ecology 144, 191–199
Tenenbaum, J.B., de Silva, V. & Langford, J.C. (2000) A global network framework for nonlinear dimensionality reduction. Science 290, 2319–2323.
The underlying functions that do the proper work are
stepacross
, distconnected
and cmdscale
.
Package mvpart provides a parallel (but a bit different) implementation
(xdiss
). Moreover, vegan function
metaMDS
may trigger stepacross
transformation, but usually only for longest dissimilarities. The
plot
method of vegan minimum spanning tree function
(spantree
) has even more extreme way of isomapping things.
## The following examples also overlay minimum spanning tree to ## the graphics in red. op <- par(mar=c(4,4,1,1)+0.2, mfrow=c(2,2)) data(BCI) dis <- vegdist(BCI) tr <- spantree(dis) pl <- ordiplot(cmdscale(dis), main="cmdscale") lines(tr, pl, col="red") ord <- isomap(dis, k=3) ord pl <- plot(ord, main="isomap k=3") lines(tr, pl, col="red") pl <- plot(isomap(dis, k=5), main="isomap k=5") lines(tr, pl, col="red") pl <- plot(isomap(dis, epsilon=0.45), main="isomap epsilon=0.45") lines(tr, pl, col="red") par(op) ## The following command requires user interaction ## Not run: rgl.isomap(ord, size=4, color="hotpink") ## End(Not run)