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] 0.09479566
fast_median(1:100)
#> [1] 50.5
x <- matrix(rnorm(100), ncol = 2)
fast_mvquantile(x, 0.2)
#> [1] -0.7605026 -0.8275685
fast_mvmedian(x)
#> [1] -0.0474960 -0.1095513
# 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 0.084778 0.127543 0.1434242 0.1461075 0.1630690 0.216053 100
#> base_median 0.096650 0.121396 0.1331405 0.1313095 0.1434775 0.241451 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 0.717167 0.756391 0.8016168 0.783005 0.818817 1.001808 10
#> base_median 2.667354 2.697770 2.7552132 2.732290 2.770055 3.016384 10