bigutilsr: hist_out – R documentation

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hist_out

Outlier detection (histogram)

Description

Outlier detection based on departure from histogram. Suitable for compact values (need a space between main values and outliers).

Usage

hist_out(x, breaks = nclass.scottRob, pmax_out = 0.2, nboot = NULL)

Arguments

`x`	Numeric vector (with compact values).
`breaks`	Same parameter as for `hist()`. Default uses a robust version of Scott's rule. You can also use `"FD"` or `nclass.FD` for a bit more bins.
`pmax_out`	Percentage at each side that can be considered outliers at each step. Default is `0.2`.
`nboot`	Number of bootstrap replicates to estimate limits more robustly. Default is `NULL` (no bootstrap, even if I would recommend to use it).

Value

A list with

x: the initial vector, whose outliers have been removed,
lim: lower and upper limits for outlier removal,
all_lim: all bootstrap replicates for lim (if nboot not NULL).

Examples

set.seed(1)
x <- rnorm(1000)
str(hist_out(x))

# Easy to separate
x2 <- c(x, rnorm(50, mean = 7))
hist(x2, breaks = nclass.scottRob)
str(hist_out(x2))

# More difficult to separate
x3 <- c(x, rnorm(50, mean = 6))
hist(x3, breaks = nclass.scottRob)
str(hist_out(x3))
str(hist_out(x3, nboot = 999))

bigutilsr

Utility Functions for Large-scale Data

v0.3.4

GPL-3

Authors

Florian Privé [aut, cre]

Initial release

2021-04-08