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Backend-dispatched covariance matrix

Source: R/models-lm.R
covariance.Rd

Computes the (possibly weighted) sample or population covariance matrix of X. Sparse inputs use a memory-efficient path; dense inputs use a fused GPU kernel when available, otherwise fall back to CPU centering followed by a GPU cross-product.

Usage

covariance(X, center = TRUE, sample = TRUE, weights = NULL, block_size = NULL)

Arguments

X

Numeric matrix, adgeMatrix, or sparse adgCMatrix / sparseMatrix, shape [n, p].

center

Logical. When TRUE (default), columns are mean-centred before computing the cross-product.

sample

Logical. When TRUE (default), divides by n - 1 (sample covariance); when FALSE, divides by n.

weights

Optional numeric vector of length n with non-negative observation weights. When supplied, a weighted covariance is computed.

block_size

Optional integer. When set, the cross-product is computed in column-blocks of this size to limit peak memory. Ignored for sparse inputs.

Value

An adgeMatrix of shape [p, p].

See also

many_lm

Examples

X <- matrix(rnorm(200), nrow = 40)
C <- covariance(X)
dim(C)
#> [1] 5 5