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
andfast_median
, and column-major matrix forfast_mvquantile
andfast_mvmedian
- prob
a probability with value from 0 to 1
- na.rm
logical; if true, any
NA
are removed fromx
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