fastcov2: Calculate Covariance Matrix in Parallel In dipsaus: A Dipping Sauce for Data Analysis and Visualizations

Description

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

Usage

 1 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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 # Get numbers of threads to 2 RcppParallel::setThreadOptions(numThreads = 2) 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 Sept. 6, 2021, 5:08 p.m.