Speed up covariance calculation for large matrices. The default behavior is the same as cov ('pearson', no NA handling).

## Usage

fast_cov(x, y = NULL, col_x = NULL, col_y = NULL, df = NA)

## Arguments

x

a numeric vector, matrix or data frame; a matrix is highly recommended to maximize the performance

y

NULL (default) or a vector, matrix or data frame with compatible dimensions to x; the default is equivalent to y = x

col_x

integers indicating the subset indices (columns) of x to calculate the covariance, or NULL to include all the columns; default is NULL

col_y

integers indicating the subset indices (columns) of y to calculate the covariance, or NULL to include all the columns; default is NULL

df

a scalar indicating the degrees of freedom; default is nrow(x)-1

## Value

A covariance matrix of x and y. Note that there is no NA handling. Any missing values will lead to NA in the resulting covariance matrices.

## Examples


# Set ncores = 2 to comply to CRAN policy. Please don't run this line

x <- matrix(rnorm(400), nrow = 100)

# Call cov(x) to compare
fast_cov(x)
#>             [,1]        [,2]        [,3]        [,4]
#> [1,]  1.17573362 -0.03815376 -0.02612938  0.09084506
#> [2,] -0.03815376  0.98190035 -0.04280019  0.04419732
#> [3,] -0.02612938 -0.04280019  1.17968697 -0.02620016
#> [4,]  0.09084506  0.04419732 -0.02620016  0.92915144

# Calculate covariance of subsets
fast_cov(x, col_x = 1, col_y = 1:2)
#>          [,1]        [,2]
#> [1,] 1.175734 -0.03815376

if(interactive()){

# Speed comparison, better to use multiple cores (4, 8, or more)
# to show the differences.

x <- matrix(rnorm(100000), nrow = 1000)
microbenchmark::microbenchmark(
fast_cov = {
fast_cov(x, col_x = 1:50, col_y = 51:100)
},
cov = {
cov(x[,1:50], x[,51:100])
},
unit = 'ms', times = 10
)

}