Outlier detection (histogram)

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

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))
#> List of 2
#>  $ x  : num [1:1000] -0.626 0.184 -0.836 1.595 0.33 ...
#>  $ lim: num [1:2] -Inf Inf

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

str(hist_out(x2))
#> List of 2
#>  $ x  : num [1:1000] -0.626 0.184 -0.836 1.595 0.33 ...
#>  $ lim: num [1:2] -Inf 4.25

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

str(hist_out(x3))
#> List of 2
#>  $ x  : num [1:1050] -0.626 0.184 -0.836 1.595 0.33 ...
#>  $ lim: num [1:2] -Inf Inf
str(hist_out(x3, nboot = 999))
#> List of 3
#>  $ x      : num [1:1007] -0.626 0.184 -0.836 1.595 0.33 ...
#>  $ lim    : num [1:2] -Inf 4.75
#>  $ all_lim: num [1:2, 1:999] -Inf 3.25 -Inf 3.25 -Inf ...