A wrapper for the roll_hampel() function that counts outliers using either a user specified threshold value or a threshold value based on the statistics of the incoming data.

  width = 25,
  thresholdMin = 7,
  selectivity = NA,
  fixedThreshold = TRUE



Numeric vector.


Integer width of the rolling window.


Numeric threshold for outlier detection


Value between [0-1] used in determining outliers, or NA if fixedThreshold=TRUE.


Logical specifying whether outlier detection uses selectivity (see Details).


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


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 statistics of 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().


This function is copied from the seismicRoll package.

See also


# 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)
#> [1] 552 766 795 1613 1761 2167 2245 2794 3086 3565 3636 4689 5329 5638 5885 #> [16] 6483 6877 7809 8182 9411
o_indices <- findOutliers(a) o_indices
#> [1] 552 766 795 1613 2245 3086 3565 3636 4689 5329 5638 5885 6483 7809 8182 #> [16] 9411
points(o_indices, a[o_indices], pch = 16, cex = 0.8, col = 'red')
title("Outlier detection using a Hampel filter")