OG: Orthogonal to Groups analysis

In emanuelealiverti/SOG:

Description

Perform matrix decomposition under group constraints.

Usage

OG(X, z, K = max(2, round(NCOL(X)/10)), rescale = T)

Arguments

X	n x p data matrix to preprocess.
z	n x 1 vector with group information.
K	approximation rank. The defaul is max(2, round(NCOL(X)/100))
rescaled	Should the matrix X be rescaled? Default is TRUE.

Details

The function performs a matrix decomposition of the input matrix X with constraints on the left singular vectors and the group variable z. The output is a matrix U of basis and a matrix S of scores such that \tilde X = S * U^T, where \tilde X is the rank-K approximation of X

Value

• U n x K matrix of scores.

• S K x p matrix of sparse loadings

References

Aliverti, Lum, Johndrow and Dunson (2018). Removing the influence of a group variable in high-dimensional predictive modelling (https://arxiv.org/abs/1810.08255).

Examples

k.rid = 10
n = 5000
p = 200
W = matrix(rnorm(p*k.rid), k.rid)
S = matrix(rnorm(n*k.rid), n)
z = sample(rep(0:1, each=n/2))
lambda = rnorm( k.rid, mean = 0, sd = 1)
A = jitter( (S - lambda * z ) %*% W)
res = OG(X = A, z = z, K = 30)

emanuelealiverti/SOG documentation built on Nov. 20, 2019, 12:45 a.m.