standardizeX: Standardization of multi-view data.

In irinagain/SLIDE: Structural Learning and Integrative Decomposition of Multi-View Data

Description

Performs column-centering and standardization so that the Frobenius norm within each view is equal to one.

Usage

standardizeX(X, pvec, center = T)

Arguments

X	A n x p concatenated data matrix of views X_1,...,X_d.
pvec	A vector of values p_1,....,p_d corresponding to the number of measurements within each view.
center	A logical indicator of whether the columns of X should be centered. The default value is TRUE.

Value

A list with the elements

X	A n x p concatenated data matrix that has been column-centered and standardized so that Frobenius norm within each view is equal to one.
svec	A vector of largest singular values for each view after centering and standardization.
norms	A vector of Frobenius norms for each view before scaling.
Xmean	A vector of column means of X before centering. A zero vector is returned if center = F.

Examples

n = 100
p1 = 40
p2 = 60
X1 = matrix(rnorm(n*p1), n, p1)
X2 = matrix(rnorm(n*p2), n, p2)
X = cbind(X1, X2)
out = standardizeX(X, pvec = c(p1,p2))

irinagain/SLIDE documentation built on Aug. 14, 2021, 2:56 p.m.