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] 0.0865877
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 0.080330 0.1232845 0.1349859 0.1366240 0.1525995 0.175417 100
#> base_median 0.160359 0.1726565 0.1802879 0.1785975 0.1847640 0.269202 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 0.668285 0.729399 0.8680743 0.8121565 0.951073 1.446087 10
#> base_median 2.769742 2.781884 2.8475650 2.7995775 2.823192 3.254305 10