adaptivegpca: Adaptive gPCA

In adaptiveGPCA: Adaptive Generalized PCA

Adaptive gPCA

Description

Performs adaptive generalized PCA, a dimensionality-reduction method which takes into account similarities between the variables. See Fukuyama, J. (2017) for more details.

Usage

adaptivegpca(X, Q, k = 2, weights = rep(1, nrow(X)))

Arguments

X	A n \times p data matrix.
Q	A p \times p similarity matrix on the variables defining an inner product on the rows of X, can also be given as an eigendecomposition (formatted as the output from eigen).
k	The number of components to return.
weights	A vector of length n containing weights for the rows of X.

Value

A list containing the row/sample scores (U), the variable loadings (QV), the proportion of variance explained by each of the principal components (vars), the value of r that was used (r).

Examples

data(AntibioticSmall)
out.agpca = adaptivegpca(AntibioticSmall$X, AntibioticSmall$Q, k = 2)

adaptiveGPCA documentation built on Dec. 8, 2022, 5:12 p.m.