mad_outlier: Mark possible outliers using different methods.
Description
Mark possible outliers in a numeric vector using various methods.
These functions return a logical vector indicating which values are outliers.
Usage
mad_outlier(x, threshold = 1.4826 * 3)
iqr_outlier(x, threshold = 1.5)
zscore_outlier(x, threshold = 3)
Value
A logical vector indicating which values are outliers.
Arguments
x
A numeric vector.
threshold
The threshold value for detecting outliers. Defaults depend on the method:
For MAD method: 1.4826 * 3 (approximately 3 standard deviations)
For IQR method: 1.5 (Tukey's rule)
For Z-score method: 3 (3 standard deviations)
Details
MAD method: Uses median absolute deviation to identify outliers.
Values with absolute deviation from the median greater than the threshold are considered outliers.
IQR method: Uses interquartile range to identify outliers.
Values below Q1 - threshold * IQR or above Q3 + threshold * IQR are considered outliers.
Z-score method: Uses standardized Z-scores to identify outliers.
Values with an absolute Z-score greater than the threshold are considered outliers.