fastcov2: Calculate Covariance Matrix in Parallel

Description Usage Arguments Value Examples

View source: R/cpp-fastcov2.R

Description

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

Usage

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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

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# 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.