fast_cov: Calculate massive covariance matrix in parallel

In ravetools: Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data

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
)

ravetools documentation built on Sept. 11, 2024, 9:06 p.m.