fast_cov {ravetools}

Calculate massive covariance matrix in parallel

Description

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)

# Calculate covariance of subsets
fast_cov(x, col_x = 1, col_y = 1:2)



# 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
)