gpuCor: Calculate Various Correlation Coefficients With a GPU

In gputools: GPU Drop-Ins for Statistics on Wide Data

Calculate Various Correlation Coefficients With a GPU

Description

Correlates each column of x with each column of y, as cor does, with Pearson's, Kendall's (tau-b) or Spearman's coefficient, and tests each correlation for zero, as cor.test does.

Usage

gpuCor(x, y = NULL, use = "everything", method = "pearson",
		test = FALSE, precision = getOption("gputools.precision", "single"))

Arguments

x	a numeric matrix whose columns are the variables.
y	a numeric matrix with the same number of rows, or NULL for x with itself.
use	"everything" (a variable with a missing value gives NA) or "pairwise.complete.obs" (each pair of variables uses the rows where both are present), as with cor.
method	"pearson", "kendall" or "spearman".
test	TRUE to also return p-values (and Kendall's z), as cor.test would. They are computed on the CPU, at about 0.3 microseconds each: negligible for hundreds of variables, but for thousands (millions of pairs) they take longer than the correlations.
precision	"single" or "double": the floating point precision the GPU computes in. Single precision is the default, set with options(gputools.precision = "double"); it runs many times faster on most consumer GPUs. Results are returned as doubles either way.

Details

Kendall's coefficient is tau-b and Spearman's is Pearson's on the ranks, ties averaged; with use = "pairwise.complete.obs", each pair is ranked over its complete rows only. All three match cor.

The p-values are two-sided and match cor.test: exactly for Pearson's (a t test on pairs - 2 degrees of freedom), and as cor.test(exact = FALSE) gives them for Spearman's (the same t approximation) and Kendall's (a normal approximation with the tie correction). cor.test's exact small-sample tests for the rank coefficients are not computed.

Value

A list of matrices, each with a row per column of x and a column per column of y, named as cor names them:

coefficients	the correlations.
ts	for Pearson's and Spearman's, the t statistics, r \sqrt{(n - 2) / (1 - r^2)}.
z	for Kendall's, with test = TRUE, the normal statistics.
p.value	with test = TRUE, the two-sided p-values.
pairs	the number of rows each correlation used.

See Also

cor, cor.test

Examples

numAvars <- 5
numBvars <- 10
numSamples <- 30
A <- matrix(runif(numAvars*numSamples), numSamples, numAvars)
B <- matrix(runif(numBvars*numSamples), numSamples, numBvars)
gpuCor(A, B, method="pearson")
gpuCor(A, B, method="kendall")
gpuCor(A, B, method="spearman", test=TRUE)$p.value
A[3,2] <- NA
gpuCor(A, B, use="pairwise.complete.obs", method="pearson")

gputools documentation built on Oct. 24, 2026, 5:07 p.m.