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().