Description Usage Arguments Details Value
Compute dimensionality reduction for subsets of the input data
1 2 3 |
Y |
\[N x P\] data matrix for which the dimensionality of P should be reduced, |
seed |
[integer] seed to initialise random number generator for drawing subsets of Y. |
size |
[double] proportion of samples from total number of samples to to choose for each subset. |
nrSubsets |
[integer] number of subsets to generate and apply dimensionality reduction to. |
method |
dimensionality reduction method [character] to be applied; one of DiffusionMap, DRR, ICA, LLE, Isomap, LaplacianEigenmap, MDS, PCA, kPCA, nMDS, tSNE and PEER. |
optN |
optimal number [integer] of neighbours to consider for dimensionality
reduction; relevant for methods LLE, LaplacianEigenmaps, Isomap and tSNE. If
not provided, will be estimated via |
ndim |
maximum dimensionality [int] to retain in the data; large values can cause long computation times; if not provided max(P,N) is chosen. |
kmin |
if optN is not provided, kmin [int] specifies the minimum number
of neighbours supplied to |
kmax |
if optN is not provided, kmax [int] specifies the maximum number
of neighbours supplied to |
verbose |
[logical] If set, progress messages are printed to standard out. |
parallel |
if optN is not provided and parallel TRUE, parallel
computation on multiple cpu cores is used with |
is.list.ellipsis |
[logical] if ... arguments are provided as list, set TRUE. |
... |
Additional arguments passed to dimensionality reduction methods. For possible arguments, check function decomentation. See details for relevant packages and functions. |
subsetDimReduction 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
* PEER: PEER
list of size nrSubsets, containing at each entry a named list of
results from computeDimReduction
:
Y_red: named list with dimensionality reduced phenotypes (reducedY) and
object returned by specified dimensionality reduction method (results) with
additional output
M: vector [double] with Trustworthiness and Continuity estimates for the
dimensionality reduction
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