funop: Identifies outliers in a numeric vector

Description Usage Arguments Value References See Also Examples

View source: R/funop.R

Description

FUNOP stands for FUll NOrmal Plot.

The procedure identifies outliers by calculating their slope (z), 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 an outlier.

However, the procedure will not identify as outliers any values within A standard deviations of the vector's median (i.e., not the median of candidate slopes).

Usage

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funop(x, A = 0, B = 1.5)

Arguments

x

Numeric vector to inspect for outliers (does not need to be ordered)

A

Number of standard deviations beyond the median of x

B

Multiples beyond the median slope of candidate values

Value

A data frame containing one row for every member of x (in the same order as x) and the following columns:

Additionally, the data frame will have the following attributes, which FUNOP calculates as part of its procedure:

References

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.

See Also

a_qnorm()

Examples

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funop(c(1, 2, 3, 11))
funop(table_1)

attr(funop(table_1), 'z_split')

Sielinski/vacuum documentation built on Sept. 15, 2020, 11:32 a.m.