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Project Out Confound Variables

Source: R/lss.R
project_confounds.Rd

Computes the orthogonal projection matrix Q = I - X(X'X)^(-1)X' that projects out the space spanned by confound regressors X. This is useful for advanced users who want to cache and reuse projection matrices.

Usage

project_confounds(X)

Arguments

X

Confound design matrix (n x p) where n is number of timepoints and p is number of confound regressors

Value

Projection matrix Q (n x n) that projects out the column space of X

Details

This function uses QR decomposition for numerical stability instead of computing the Moore-Penrose pseudoinverse directly. Only the estimated-rank QR basis is used, so redundant confound columns do not over-project. The resulting matrix Q can be applied to data to remove the influence of confound regressors.

Examples

# \donttest{
n <- 100
X_confounds <- cbind(1, 1:n)
Y_raw <- matrix(rnorm(n * 3), n, 3)

Q <- project_confounds(X_confounds)

Y_clean <- Q %*% Y_raw
# }