Check for Outliers — outlier

Detects univariate and multivariate outliers using various methods including z-score (2SD, 3SD, etc.), IQR, percentile, or Mahalanobis distance.

Usage

outlier_check(
  data,
  Vars,
  method = c("zscore", "iqr", "percentile", "mahalanobis"),
  threshold = NULL,
  flag = TRUE,
  plot = TRUE
)

Arguments

data: A data frame containing the variables.
Vars: Character vector of numeric variable names to check.
method: Detection method: "zscore", "iqr", "percentile", or "mahalanobis".
threshold: Numeric threshold for outlier detection. For zscore: number of SDs (default 3). Common values: 2, 2.5, 3, 3.29. For iqr: IQR multiplier (default 1.5). Common values: 1.5, 3. For percentile: percentile cutoff (default 0.01 for 1st/99th). Values like 0.05 for 5th/95th. For mahalanobis: chi-sq p-value threshold (default 0.001).
flag: Logical. Add outlier flag column to returned data? Default TRUE.
plot: Logical. Create outlier visualization? Default TRUE.

Value

A list containing:

summary: gt table with outlier summary
data: Data frame with outlier flags (if flag = TRUE)
outlier_indices: Row indices of outliers
plot: Visualization (if requested)

Examples

if (FALSE) { # \dontrun{
# Z-score with 3 SD threshold
outlier_check(mtcars, Vars = c("mpg", "hp"), method = "zscore", threshold = 3)

# Z-score with 2 SD threshold (more conservative)
outlier_check(mtcars, Vars = c("mpg", "hp"), method = "zscore", threshold = 2)

# IQR method
outlier_check(mtcars, Vars = c("mpg", "hp"), method = "iqr", threshold = 1.5)

# Percentile method (1st and 99th percentile)
outlier_check(mtcars, Vars = c("mpg", "hp"), method = "percentile", threshold = 0.01)

# Percentile method (5th and 95th percentile)
outlier_check(mtcars, Vars = c("mpg", "hp"), method = "percentile", threshold = 0.05)

# Mahalanobis distance (multivariate)
outlier_check(mtcars, Vars = c("mpg", "hp", "wt"), method = "mahalanobis")
} # }