reform_decomp: Recompute eclairs after dropping features

In decorrelate: Decorrelation Projection Scalable to High Dimensional Data

Recompute eclairs after dropping features

Description

Recompute eclairs after dropping features

Usage

reform_decomp(ecl, k = ecl$k, drop = NULL)

Arguments

ecl	covariance/correlation matrix as an eclairs object
k	the rank of the low rank component
drop	array of variable names to drop.

Details

Reform the dataset from the eclairs decomposition, drop features, then recompute the eclairs decomposition. If the original SVD/eigen was truncated, then the reconstruction of the original data will be approximate. Note that the target shrinkage matrix is the same as in ecl, so \nu is not recomputed from the retained features.

Value

eclairs decomposition for a subset of features

Examples

library(Rfast)

n <- 800 # number of samples
p <- 200 # number of features

# create correlation matrix
Sigma <- autocorr.mat(p, .9)

# draw data from correlation matrix Sigma
Y <- rmvnorm(n, rep(0, p), sigma = Sigma * 5.1, seed = 1)
rownames(Y) <- paste0("sample_", seq(n))
colnames(Y) <- paste0("gene_", seq(p))

# Correlation
#------------

# eclairs decomposition
Sigma.eclairs <- eclairs(Y, compute = "correlation")

# features to drop
drop <- paste0("gene_", 1:100)

# Compute SVD on subset of eclairs decomposition
ecl1 <- reform_decomp(Sigma.eclairs, drop = drop)

ecl1

decorrelate documentation built on Aug. 8, 2025, 7:55 p.m.