Calculate massive covariance matrix in parallel

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
ravetools_threads(n_threads = 2L)

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

# Call `cov(x)` to compare
fast_cov(x)
#>             [,1]        [,2]        [,3]        [,4]
#> [1,]  0.89287065  0.04864466 -0.02881969  0.01654759
#> [2,]  0.04864466  0.94437950 -0.12577546  0.01717759
#> [3,] -0.02881969 -0.12577546  0.87766960 -0.25643174
#> [4,]  0.01654759  0.01717759 -0.25643174  0.91763758

# Calculate covariance of subsets
fast_cov(x, col_x = 1, col_y = 1:2)
#>           [,1]       [,2]
#> [1,] 0.8928707 0.04864466

# \donttest{

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

ravetools_threads(n_threads = -1)
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
)
#> Unit: milliseconds
#>      expr      min       lq     mean   median       uq      max neval
#>  fast_cov 1.469737 1.485350 1.647569 1.503451 1.528674 2.951971    10
#>       cov 6.576223 6.582833 6.595625 6.590279 6.600439 6.653628    10

# }