Description Usage Arguments Details Value
Wrapper function for dimensionality reduction methods
1 2 3 4 |
Y |
\[N x P\] data matrix for which the dimensionality of P should be reduced |
ndim |
maximum dimensionality [integer] to retain in the data; large values can cause long computation times. |
distY |
[dist] object of class dist containing pairwise distances of Y used for methods DiffusionMap, Isomap, MDS and nMDS; if non specified, stats::dist with Euclidean distance applied to supplied Y. |
dist.method |
[character] method for computing the distance matrix; one of
euclidean, maximum, manhattan, canberra, binary or minkowski; see
|
method |
Dimensionality reduction method [character] to be applied; one of DiffusionsMaps, DRR, ICA, LLE, Isomap, LaplacianEigenmap, MDS, PCA, kPCA, nMDS and tSNE. |
optN |
optimal number [integer] of neighbours to consider for dimensionality reduction; relevant for methods LLE, LaplacianEigenmaps, Isomap and tSNE. |
verbose |
[logical] If set, progress messages are printed to standard out. |
params |
[list] optional additional parameters for dimensionality reduction methods; see details. |
methodsDimReduction wraps around the following implementations of
the dimensionality reduction methods it provides:
* Diffusion Map: diffuse
* Dimensionality reduction by regression (DRR): drr
* Independent component analysis (ICA): fastICA
* Local liner embedding (LLE): lle
* Isomap: isomap
* Laplacian Eigenmap: spec.emb
and
make.kNNG
* Multi-dimensional scaling (MDS) : cmdscale
* Principal component analysis (PCA): prcomp
* Kernel PCA (kPCA): kpca
* non-metrix MDS (nMDS): metaMDS
* t- stochastic neighbourhood embedding (tSNE): Rtsne
* Uniform manifold approximation and projection (umap):
umap
named list with dimensionality reduced phenotypes (reducedY) and object returned by specified dimensionality reduction method (results) with additional output, see details.
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