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Detect outliers in a numeric vector

Source: R/statistics.R
detect_outliers.Rd

Three standard rules:

  • "iqr": outside Q1 - k*IQR / Q3 + k*IQR (Tukey's rule, k = 1.5).

  • "zscore": absolute z-score above threshold (default 3).

  • "robust": absolute modified z-score using the median and MAD above threshold (default 3.5); resistant to the outliers it is detecting.

Usage

detect_outliers(x, method = c("iqr", "zscore", "robust"), threshold = NULL)

Arguments

x

A numeric vector.

method

One of "iqr", "zscore", "robust".

threshold

Cutoff for "zscore"/"robust"; the IQR multiplier for "iqr". Defaults: 1.5 (iqr), 3 (zscore), 3.5 (robust).

Value

A list: method, n (non-missing count), n_outliers, pct, is_outlier (a logical vector aligned to x, FALSE for NA), and bounds (lower/upper, where applicable).

Examples

detect_outliers(c(1, 2, 3, 4, 100), method = "iqr")
#> $method
#> [1] "iqr"
#> 
#> $n
#> [1] 5
#> 
#> $n_outliers
#> [1] 1
#> 
#> $pct
#> [1] 20
#> 
#> $is_outlier
#> [1] FALSE FALSE FALSE FALSE  TRUE
#> 
#> $bounds
#> lower upper 
#>    -1     7 
#>