W.QR: Build and decompose a low-rank matrix W

In DrBats: Data Representation: Bayesian Approach That's Sparse

Build and decompose a low-rank matrix W

Description

Build and decompose a low-rank matrix from a matrix of eigenvectors and eigenvalues from principal component analysis

Usage

W.QR(U, lambda)

Arguments

U	a matrix of eigenvectors
lambda	a vector of corresponding eigenvalues

Value

W a low-rank matrix

D the number of latent factors

Q the orthogonal matrix of the W = QR matrix decomposition

R the upper triangular matrix of the W = QR matrix decomposition

Author(s)

Gabrielle Weinrott

Examples

res <- drbats.simul(N = 5, P = 100, t.range = c(5, 100), breaks = 8)
res.pca <- pca.Deville(res$X, res$t.simul, t.range = c(5, 100), breaks = 8)
Wres.pca <- W.QR(res.pca$U, res.pca$lambda)
Wres.pca

DrBats documentation built on March 18, 2022, 5:15 p.m.