Non-Metric Dimensional Scaling
An S4 Class implementing Non-Metric Dimensional Scaling.
A non-linear extension of MDS using monotonic regression
funA function that does the embedding and returns a dimRedResult object.
stdparsThe standard parameters for the function.
Dimensionality reduction methods are S4 Classes that either be used
directly, in which case they have to be initialized and a full
list with parameters has to be handed to the @fun()
slot, or the method name be passed to the embed function and
parameters can be given to the ..., in which case
missing parameters will be replaced by the ones in the
@stdpars.
nMDS can take the following parameters:
A distance function.
The number of embedding dimensions.
Wraps around the
monoMDS. For parameters that are not
available here, the standard configuration is used.
Kruskal, J.B., 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29, 115-129. https://doi.org/10.1007/BF02289694
Other dimensionality reduction methods: AutoEncoder-class,
DRR-class,
DiffusionMaps-class,
DrL-class, FastICA-class,
FruchtermanReingold-class,
HLLE-class, Isomap-class,
KamadaKawai-class, LLE-class,
MDS-class, NNMF-class,
PCA-class, PCA_L1-class,
UMAP-class,
dimRedMethod-class,
dimRedMethodList, kPCA-class,
tSNE-class
dat <- loadDataSet("3D S Curve", n = 300)
## using the S4 classes:
nmds <- nMDS()
emb <- nmds@fun(dat, nmds@stdpars)
## using embed()
emb2 <- embed(dat, "nMDS", d = function(x) exp(dist(x)))
plot(emb, type = "2vars")
plot(emb2, type = "2vars")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.