gpca: GPCA

In oliviermfmartin/HelpingHand: A helping hand

Description

Group-wise sparse principal component analysis

Usage

gpca(
  x,
  groups,
  y = NULL,
  lambda = NULL,
  scale = FALSE,
  center = TRUE,
  sign_correction = TRUE,
  return_data = FALSE,
  trace = FALSE,
  kernel = "linear",
  bandwidth = NULL
)

Arguments

x	numeric matrix; data with variables as columns and samples as rows.
groups	boolean matrix; affiliation matrix. Does the variable in row i pertain to the group in column j?
y	numeric matrix; confounder matrix with rows as samples and columns as confounding factors.
lambda	the tuning parameter, non-negative.
scale	logical; should the data be scaled?
sign_correction	logical; should signCorrectionPCA be called?
return_data	logical; should data be returned?
trace	logical; should progress be printed?
kernel	the kernel to use: "linear", "gaussian".
bandwidth	bandwidth h for Gaussian kernel. Optional.

Value

A list with scores and loadings.

oliviermfmartin/HelpingHand documentation built on Oct. 10, 2020, 5:59 a.m.