### 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.