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] 0.08398884
fast_median(1:100)
#> [1] 50.5

x <- matrix(rnorm(100), ncol = 2)
fast_mvquantile(x, 0.2)
#> [1] -0.6648696 -0.9950019
fast_mvmedian(x)
#> [1] 0.25158133 0.01044387

# 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 0.080881 0.123555 0.1406104 0.144314 0.1600030 0.192178   100
#>  base_median 0.171850 0.182405 0.1903131 0.188652 0.1945525 0.288648   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 0.885000 1.024560 1.116767 1.095714 1.185310 1.443702    10
#>  base_median 2.758714 2.806733 2.909456 2.881457 3.012506 3.163098    10