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.
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]
#> 5 x 5 sparse Matrix of class "dsCMatrix"
#> col_1 col_2 col_3 col_4 col_5
#> col_1 1.00000000 0.03982146 0.03290085 -0.02022058 0.00827069
#> col_2 0.03982146 1.00000000 -0.04172518 -0.00276169 -0.03594182
#> col_3 0.03290085 -0.04172518 1.00000000 0.03481704 -0.03144989
#> col_4 -0.02022058 -0.00276169 0.03481704 1.00000000 -0.05034769
#> col_5 0.00827069 -0.03594182 -0.03144989 -0.05034769 1.00000000
c[1:5, 1:5]
#> 5 x 5 sparse Matrix of class "dsCMatrix"
#> col_1 col_2 col_3 col_4 col_5
#> col_1 1.00000000 0.03982146 0.03290085 -0.02022058 0.00827069
#> col_2 0.03982146 1.00000000 -0.04172518 -0.00276169 -0.03594182
#> col_3 0.03290085 -0.04172518 1.00000000 0.03481704 -0.03144989
#> col_4 -0.02022058 -0.00276169 0.03481704 1.00000000 -0.05034769
#> col_5 0.00827069 -0.03594182 -0.03144989 -0.05034769 1.00000000
all.equal(a, c)
#> [1] "Attributes: < Component “i”: Numeric: lengths (5047, 5050) differ >"
#> [2] "Attributes: < Component “p”: Mean relative difference: 0.0008422367 >"
#> [3] "Attributes: < Component “x”: Numeric: lengths (5047, 5050) differ >"
b[1:5, 1:5]
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#> col_1 col_2 col_3 col_4 col_5
#> col_1 0.003888632 -0.036322389 0.03054830 0.04990672 0.053832055
#> col_2 -0.042598718 -0.044702907 0.05688602 -0.02310432 0.007310609
#> col_3 -0.003132754 -0.041963945 -0.04085159 -0.03183016 -0.028050257
#> col_4 0.045648545 0.009914274 0.01685729 0.03317848 -0.045443563
#> col_5 -0.048082595 -0.050457664 0.04350114 -0.05551273 -0.051113224
d[1:5, 1:5]
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#> col_1 col_2 col_3 col_4 col_5
#> col_1 0.003888632 -0.036322389 0.03054830 0.04990672 0.053832055
#> col_2 -0.042598718 -0.044702907 0.05688602 -0.02310432 0.007310609
#> col_3 -0.003132754 -0.041963945 -0.04085159 -0.03183016 -0.028050257
#> col_4 0.045648545 0.009914274 0.01685729 0.03317848 -0.045443563
#> col_5 -0.048082595 -0.050457664 0.04350114 -0.05551273 -0.051113224
all.equal(b, d)
#> [1] "Attributes: < Component “i”: Numeric: lengths (9997, 10000) differ >"
#> [2] "Attributes: < Component “p”: Mean relative difference: 0.000311165 >"
#> [3] "Attributes: < Component “x”: Numeric: lengths (9997, 10000) differ >"
m1[sample(1:500, 10)] <- NA
m2[sample(1:500, 10)] <- NA
sparse_cor(m1, m2)[1:5, 1:5]
#> 5 x 5 sparse Matrix of class "dgCMatrix"
#> col_1 col_2 col_3 col_4 col_5
#> col_1 . . . . .
#> col_2 . -0.044702907 0.05688602 -0.02310432 0.007310609
#> col_3 . -0.041963945 -0.04085159 -0.03183016 -0.028050257
#> col_4 . 0.009914274 0.01685729 0.03317848 -0.045443563
#> col_5 . -0.050457664 0.04350114 -0.05551273 -0.051113224
system.time(
sparse_cor(m1)
)
#> user system elapsed
#> 0.003 0.000 0.003
system.time(
cor(as_matrix(m1))
)
#> user system elapsed
#> 0.006 0.000 0.006
system.time(
sparse_cor(m1, m2)
)
#> user system elapsed
#> 0.002 0.000 0.001
system.time(
cor(as_matrix(m1), as_matrix(m2))
)
#> user system elapsed
#> 0.01 0.00 0.01