Trimmed and winsorized means (C backend)

Two ways to stop a handful of extreme rows dominating a column's centre. core_trimmed_mean() DISCARDS the floor(n * trim) largest and smallest values, matching mean(x, trim = ). core_winsorized_mean() instead PULLS THEM IN to the most extreme surviving values, so every observation still contributes weight – usually the better choice when the extremes are real measurements rather than errors.

Usage

core_trimmed_mean(x, trim = 0.1)

core_winsorized_mean(x, trim = 0.1)

Arguments

x

Numeric vector (coerced with as.numeric()) .

trim

Proportion trimmed from each end, in \ [0, 0.5]. At 0.5 both reduce to the median.

[0, 0.5]: R:0,%200.5%5C

Value

A length-1 numeric.

Examples

x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 100)

mean(x)                               # dragged up by the 100
#> [1] 14.5
core_trimmed_mean(x, 0.1)             # the 100 and the 1 dropped
#> [1] 5.5
core_winsorized_mean(x, 0.1)          # the 100 pulled back to 9
#> [1] 5.5

# Agrees with base R's own trimming.
all.equal(core_trimmed_mean(x, 0.2), mean(x, trim = 0.2))
#> [1] TRUE

# trim = 0 is the plain mean; trim = 0.5 is the median.
all.equal(core_trimmed_mean(x, 0), mean(x))
#> [1] TRUE
all.equal(core_trimmed_mean(x, 0.5), core_median(x))
#> [1] TRUE