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, ...)
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] 0.08925259
fast_median(1:100)
#> [1] 50.5
x <- matrix(rnorm(100), ncol = 2)
fast_mvquantile(x, 0.2)
#> [1] -0.9291953 -1.0147020
fast_mvmedian(x)
#> [1] -0.01031475 -0.03301562
# 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 0.078276 0.1196085 0.1309507 0.1347660 0.1449095 0.185877 100
#> base_median 0.162022 0.1735680 0.1829872 0.1813625 0.1881360 0.271346 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 0.660190 0.685738 0.7452996 0.726944 0.737014 1.053423 10
#> base_median 2.674792 2.743079 2.7693537 2.763683 2.801048 2.916573 10