findOutliers: Outlier detection with a rolling Hampel filter

In MazamaRollUtils: Efficient Rolling Functions

Outlier detection with a rolling Hampel filter

Description

A wrapper around roll_hampel() that identifies outliers using either a fixed threshold or a threshold derived from the input data.

Usage

findOutliers(
  x,
  width = 25,
  thresholdMin = 7,
  selectivity = NA,
  fixedThreshold = TRUE
)

Arguments

x	Numeric vector.
width	Integer width of the rolling window.
thresholdMin	Numeric threshold for outlier detection
selectivity	Value between 0 and 1 used in determining outliers, or NA if fixedThreshold=TRUE.
fixedThreshold	Logical specifying whether outlier detection uses selectivity (see Details).

Details

The thresholdMin level is similar to a sigma value for normally distributed data. Hampel filter values above 6 indicate a data value that is extremely unlikely to be part of a normal distribution (~ 1/500 million) and therefore very likely to be an outlier. By choosing a relatively large value for thresholdMin we make it less likely that we will generate false positives. False positives can include high frequency environmental noise.

With the default setting of fixedThreshold = TRUE any value above the threshold is considered an outlier and the selectivity is ignored.

The selectivity is a value between 0 and 1 and is used to generate an appropriate threshold for outlier detection based on the Hampel filter values computed from the incoming data. A lower value for selectivity will result in more outliers, while a value closer to 1.0 will result in fewer. If fixedThreshold=TRUE, selectivity may have a value of NA.

When the user specifies fixedThreshold=FALSE, the thresholdMin and selectivity parameters work like squelch and volume on a CB radio: thresholdMin sets a noise threshold below which you don't want anything returned while selectivity adjusts the number of points defined as outliers by setting a new threshold defined by the maximum value of roll_hampel multiplied by selectivity.

width, the window width, is a parameter that is passed to roll_hampel().

Value

A vector of indices associated with outliers in the incoming data x.

Note

This function is copied from the seismicRoll package.

See Also

roll_hampel()

Examples

# Noisy sinusoid with outliers
a <- jitter(sin(0.1*seq(1e4)),amount=0.2)
indices <- sample(seq(1e4),20)
a[indices] <- a[indices]*10

# Outlier detection should identify many of these altered indices
sort(indices)
o_indices <- findOutliers(a)
o_indices

plot(a)
points(o_indices, a[o_indices], pch = 16, cex = 0.8, col = 'red')
title("Outlier detection using a Hampel filter")

MazamaRollUtils documentation built on March 17, 2026, 5:07 p.m.