Compute quantiles

Compute quantiles

Usage

fast_quantile(x, prob = 0.5, na.rm = FALSE, ...)

fast_median(x, na.rm = FALSE, ...)

fast_mvquantile(x, prob = 0.5, na.rm = FALSE, ...)

fast_mvmedian(x, na.rm = FALSE, ...)

Arguments

x

numerical-value vector for fast_quantile and fast_median, and column-major matrix for fast_mvquantile and fast_mvmedian

prob

a probability with value from 0 to 1

na.rm

logical; if true, any NA are removed from x before the quantiles are computed

...

reserved for future use

Value

fast_quantile and fast_median calculate univariate quantiles (single-value return); fast_mvquantile and fast_mvmedian calculate multivariate quantiles (for each column, result lengths equal to the number of columns).

Examples

fast_quantile(runif(1000), 0.1)
#> [1] 0.0865877
fast_median(1:100)
#> [1] 50.5

x <- matrix(rnorm(100), ncol = 2)
fast_mvquantile(x, 0.2)
#> [1] -0.8459934 -1.2524877
fast_mvmedian(x)
#> [1]  0.10661071 -0.08524222

# Compare speed for vectors (usually 30% faster)
x <- rnorm(10000)
microbenchmark::microbenchmark(
  fast_median = fast_median(x),
  base_median = median(x),
  # bioc_median = Biobase::rowMedians(matrix(x, nrow = 1)),
  times = 100, unit = "milliseconds"
)
#> Unit: milliseconds
#>         expr      min        lq      mean    median        uq      max neval
#>  fast_median 0.088025 0.1298775 0.1431134 0.1462225 0.1589065 0.190576   100
#>  base_median 0.158176 0.1765645 0.1840919 0.1838530 0.1887625 0.284400   100

# Multivariate cases
# (5~7x faster than base R)
# (3~5x faster than Biobase rowMedians)
x <- matrix(rnorm(100000), ncol = 20)
microbenchmark::microbenchmark(
  fast_median = fast_mvmedian(x),
  base_median = apply(x, 2, median),
  # bioc_median = Biobase::rowMedians(t(x)),
  times = 10, unit = "milliseconds"
)
#> Unit: milliseconds
#>         expr      min       lq      mean    median       uq      max neval
#>  fast_median 0.672245 0.717635 0.7852053 0.7255045 0.846860 1.031695    10
#>  base_median 2.747176 2.805093 2.8556388 2.8259875 2.852552 3.203968    10