sparse_cor: A sparse correlation function

In thisutils: Collection of Utility Functions for Data Analysis and Computing

A sparse correlation function

Description

Safe correlation function which returns a sparse matrix without missing values

Usage

sparse_cor(
  x,
  y = NULL,
  method = "pearson",
  allow_neg = TRUE,
  remove_na = TRUE,
  remove_inf = TRUE,
  ...
)

Arguments

x	Sparse matrix or character vector.
y	Sparse matrix or character vector.
method	Method to use for calculating the correlation coefficient.
allow_neg	Logical. Whether to allow negative values or set them to 0.
remove_na	Logical. Whether to replace NA values with 0.
remove_inf	Logical. Whether to replace infinite values with 1.
...	Other arguments passed to stats::cor function.

Value

A correlation matrix.

Examples

m1 <- simulate_sparse_matrix(
  500, 100
)
m2 <- simulate_sparse_matrix(
  500, 100,
  seed = 2025
)
a <- sparse_cor(m1)
b <- sparse_cor(m1, m2)
c <- as_matrix(
  cor(as_matrix(m1)),
  sparse = TRUE
)
d <- as_matrix(
  cor(as_matrix(m1), as_matrix(m2)),
  sparse = TRUE
)

a[1:5, 1:5]
c[1:5, 1:5]
all.equal(a, c)

b[1:5, 1:5]
d[1:5, 1:5]
all.equal(b, d)

m1[sample(1:500, 10)] <- NA
m2[sample(1:500, 10)] <- NA

sparse_cor(m1, m2)[1:5, 1:5]

system.time(
  sparse_cor(m1)
)
system.time(
  cor(as_matrix(m1))
)

system.time(
  sparse_cor(m1, m2)
)
system.time(
  cor(as_matrix(m1), as_matrix(m2))
)

thisutils documentation built on July 3, 2025, 9:09 a.m.