findOutliers: Outlier Detection with a Rolling Hampel Filter

Description

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.

Usage

findOutliers(x, n = 41, thresholdMin = 10, selectivity = NA,
  increment = 1, fixedThreshold = TRUE)

Arguments

an R numeric vector

integer window size

thresholdMin

initial value for outlier detection

selectivity

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

increment

integer shift to use when sliding the window to the next location

fixedThreshold

logical specifying whether outlier detection uses selectivity (see below)

Value

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

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 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.

n, the windowSize, is a parameter that is passed to roll_hampel().

The default value of increment=1 should not be changed. Outliers are defined as individual points that stand apart from their neighbors. Applying the Hampel filter to every other point by using increment > 1 will invariably miss some of the outliers.

Examples

Run this code

# NOT RUN {
# 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)
findOutliers(a)
# }