d_MDS: Multidimensional Scaling

In egenn/rtemis: Machine Learning and Visualization

Multidimensional Scaling

Description

Perform MDS decomposition using stats:cmdscale

Usage

d_MDS(
  x,
  k = 2,
  dist.method = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  eig = FALSE,
  add = FALSE,
  x.ret = FALSE,
  scale = TRUE,
  center = TRUE,
  verbose = TRUE,
  ...
)

Arguments

x	Input data
k	Integer vector of length 1 or greater. Rank of decomposition
dist.method	Character: method to use to calculate distance. See stats::dist("method")
eig	Logical: If TRUE, return eigenvalues. Default = FALSE
add	Logical: If TRUE, an additive constant ⁠c*⁠ will be computed and added to the non-diagonal dissimilarities, which makes the Euclidean. Default = FALSE
x.ret	Logical: If TRUE, return the doubly centered symmetric distance matrix. Default = FALSE
scale	Logical: If TRUE, scale input data before decomposition. Default = TRUE
center	Logical: If TRUE, also center input data if scale is TRUE. Default = TRUE
verbose	Logical: If TRUE, print messages to screen. Default = TRUE
...	Additional parameters to be passed to svd

Details

Project scaled variables to MDS components. Input must be n by p, where n represents number of cases, and p represents number of features. fastMDS will be applied to the transpose of the n x p matrix. fastMDS will fail if there are any NA values or constant features: remove them using preprocess

Value

rtDecom object

Author(s)

E.D. Gennatas

See Also

Other Decomposition: d_H2OAE(), d_H2OGLRM(), d_ICA(), d_Isomap(), d_KPCA(), d_LLE(), d_NMF(), d_PCA(), d_SPCA(), d_SVD(), d_TSNE(), d_UMAP()

egenn/rtemis documentation built on Dec. 17, 2024, 6:16 p.m.