clinpubr (version 1.3.0)

detect_outliers: Detect outliers in a numeric vector.

Description

Detect outliers in a numeric vector using various methods.

Usage

detect_outliers(x, method = "iqr", threshold = NULL)

Value

A list containing:

• outlier_mask: Logical vector indicating outliers, NA for missing values

• outlier_count: Number of outliers detected

• outlier_pct: Percentage of outliers in the data

• summary: Summary statistics including:

  • Before removing outliers: max, min, variance

  • After removing outliers: max, min, variance

  • Method-specific details

Arguments

x

A numeric vector.

method

The method to use for outlier detection. One of "mad", "iqr", or "zscore".

threshold

The threshold value for detecting outliers. Defaults depend on the method.

Details

This function provides a unified interface for detecting outliers using different methods.

• "mad": Median absolute deviation method

• "iqr": Interquartile range method

• "zscore": Z-score method

See Also

mad_outlier, iqr_outlier, zscore_outlier

Examples

x <- c(1, 2, 3, 4, 5, 100)
detect_outliers(x, method = "iqr")