Detect outliers using multiple methods

Identifies outliers in selected columns using one of several methods: IQR-based, Median Absolute Deviation (MAD), or percentile-based. The function can either return a logical mask indicating outlier positions or replace outliers with NA.

Usage

detect_outliers(data, cols = NULL, method = "iqr",
                top = 0.995, bottom = 0.0025, coef = 1.5,
                group = NULL, mask_only = TRUE, verbose = FALSE)

Arguments

data

A data frame, matrix, or numeric vector.

cols

The column indices or names of selected variables. If NULL, all columns are used.

method

Detection method. One of "iqr" (default), "mad", "percentile".

top

The top percentile threshold for percentile method.

bottom

The bottom percentile threshold for percentile method.

coef

The coefficient for IQR or MAD method. For IQR, values beyond Q1 - coef*IQR and Q3 + coef*IQR are outliers. For MAD, values with |z| > coef are outliers.

group

Optional grouping column for group-wise detection.

mask_only

Logical. If TRUE (default), returns a logical matrix of outlier positions. If FALSE, returns data with outliers replaced by NA.

verbose

Logical; if TRUE, prints progress message.

Details

The IQR method uses Tukey's fences: values outside [Q1 - coef*IQR, Q3 + coef*IQR] are considered outliers. The MAD method uses robust z-scores: |0.6745*(x - median)/MAD| > coef. The percentile method flags values above the top percentile or below the bottom percentile.

Value

If mask_only = TRUE, a logical matrix with TRUE indicating outliers. If mask_only = FALSE, a data frame with outliers set to NA.

Examples

# Return mask
mask <- detect_outliers(data[1:100, c(1, 4, 17:19)], cols = 3:5, method = "iqr")
# Replace outliers with NA
cleaned <- detect_outliers(data[1:100, c(1, 4, 17:19)], cols = 3:5,
                           method = "mad", mask_only = FALSE)