FUNOP stands for FUll NOrmal Plot.
The procedure identifies outliers by calculating their slope (
relative to the vector's median.
The procedure ignores values in the middle third of the ordered vector. The remaining values are all candidates for consideration. The slopes of candidate values are calculated, and the median of their slopes is used as the primary basis for identifying outliers.
Any value whose slope is
B times larger than the median slope is
identified as an outlier. Additionally, any value whose magnitude
is larger than that of the slope-based outliers is also identified as
However, the procedure will not identify as outliers any values
A standard deviations of the vector's median (i.e., not
the median of candidate slopes).
funop(x, A = 0, B = 1.5)
Numeric vector to inspect for outliers (does not need to be ordered)
Number of standard deviations beyond the median of
Multiples beyond the median slope of candidate values
A data frame containing one row for every member of
x (in the same
x) and the
y: Original values of vector
i: Ordinal position of value
y in the sorted vector
middle: Boolean indicating whether ordinal position
i is in the middle third of the vector
a: Result of
z: Slope of
y relative to
special: Boolean indicating whether
y is an outlier
Additionally, the data frame will have the following attributes, which FUNOP calculates as part of its procedure:
y_split: Median of the vector
y_trimmed: Mean of the top and bottom thirds of the ordered vector
z_split: Median slope of candidate values
Tukey, John W. "The Future of Data Analysis." The Annals of Mathematical Statistics, 33(1), 1962, pp 1-67. JSTOR, https://www.jstor.org/stable/2237638.
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