fastcov2: Calculate Covariance Matrix in Parallel

In dipsaus: A Dipping Sauce for Data Analysis and Visualizations

Calculate Covariance Matrix in Parallel

Description

Speed up covariance calculation for large matrices. The default behavior is similar cov. Please remove any NA prior to calculation.

Usage

fastcov2(x, y = NULL, col1, col2, df)

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
col1	integers indicating the subset (columns) of x to calculate the covariance; default is all the columns
col2	integers indicating the subset (columns) of y to calculate the covariance; default is all the columns
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

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

# Call `cov(x)` to compare
fastcov2(x)

# Calculate covariance of subsets
fastcov2(x, col1 = 1, col2 = 1:2)

# Speed comparison
x <- matrix(rnorm(100000), nrow = 1000)
microbenchmark::microbenchmark(
  fastcov2 = {
    fastcov2(x, col1 = 1:50, col2 = 51:100)
  },
  cov = {
    cov(x[,1:50], x[,51:100])
  },
  unit = 'ms', times = 10
)

dipsaus documentation built on June 27, 2024, 5:10 p.m.