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Detect outliers using MAD (Median Absolute Deviation)

Source: R/RunCellQC.R
is_outlier.Rd

This function detects outliers in a numeric vector using the MAD (Median Absolute Deviation) method. It calculates the median and the MAD, and determines the boundaries for outliers based on the median and the selected number of MADs.

Usage

is_outlier(
  x,
  nmads = 2.5,
  constant = 1.4826,
  type = c("both", "lower", "higher")
)

Arguments

x

Numeric vector.

nmads

Number of median absolute deviations (MADs) from the median to define the boundaries for outliers. Default is 2.5.

constant

Constant factor to convert the MAD to a standard deviation. Default is 1.4826, which is consistent with the MAD of a normal distribution.

type

Type of outliers to detect. Available options are "both", "lower", or "higher". If type is "both", it detects both lower and higher outliers. If type is "lower", it detects only lower outliers. If type is "higher", it detects only higher outliers.

Value

Numeric vector of indices indicating the positions of outliers in x.

Examples

x <- c(1, 2, 3, 4, 5, 100)
is_outlier(x) # returns 6
#> [1] 6

x <- c(3, 4, 5, NA, 6, 7)
is_outlier(x, nmads = 1.5, type = "lower") # returns 4
#> [1] 4

x <- c(10, 20, NA, 15, 35)
is_outlier(x, nmads = 2, type = "higher") # returns 3, 5
#> [1] 3 5