dimRed (version 0.2.0)

nMDS-class: Non-Metric Dimensional Scaling

Description

An S4 Class implementing Non-Metric Dimensional Scaling.

Slots

fun

A function that does the embedding and returns a dimRedResult object.

stdpars

The standard parameters for the function.

General usage

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.

Parameters

nMDS can take the following parameters:

d

A distance function.

ndim

The number of embedding dimensions.

Implementation

Wraps around the monoMDS. For parameters that are not available here, the standard configuration is used.

Details

A non-linear extension of MDS using monotonic regression

References

Kruskal, J.B., 1964. Nonmetric multidimensional scaling: A numerical method. Psychometrika 29, 115-129. https://doi.org/10.1007/BF02289694

See Also

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

Examples

# NOT RUN {
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")

# }