isoMDS(d, y = cmdscale(d, k), k = 2, niter = 50, trace = T)
d
|
distance structure of the form returned by dist , or a full, symmetric
matrix. Data are assumed to be dissimilarities or relative distances,
but must be positive except for self-distance.
|
y
|
An initial configuration. If none is supplied, cmdscale is used to provide
the classical solution.
|
k
| The dimension of the configuration. |
niter
| The maximum number of iterations. |
trace
|
Logical for tracing optimization. Default true .
|
An iterative algorithm is used, which will usually converge in around 10 iterations. As this is necessarily an O(n^2) calculation, it is slow for large datasets. Further, since the configuration is only determined up to rotations and reflections (by convention the centroid is at the origin), the result can vary considerably from machine to machine.
points
| A two-column vector of the fitted configuration. |
stress
| The final stress achieved (in percent). |
cmdscale
, sammon
data(swiss) swiss.x <- as.matrix(swiss[, -1]) swiss.dist <- dist(swiss.x) swiss.mds<-isoMDS(swiss.dist) plot(swiss.mds$points, type="n") text(swiss.mds$points, labels=as.character(1:nrow(swiss.x))) swiss.sh <- Shepard(swiss.dist, swiss.mds$points) plot(swiss.sh) lines(swiss.sh$x, swiss.sh$yf, type="S")