iglewicz_hoaglin: Detect outliers using the modified Z score method of Iglewicz...
In skinnider/modern: Model-free outlier detection for robust network inference

Description Usage Arguments Details Value

View source: R/iglewicz_hoaglin.R

Identify outliers within a distribution of numeric values using the modified Z score method. Iglewicz and Hoaglin recommend an absolute Z score threshold of 3.5 to identify potential outliers.

1	iglewicz_hoaglin(x, threshold = 3.5, return_scores = F)

`x`	distribution to find outliers in
`threshold`	absolute value of the modified z score threshold above which to consider a value an outlier; defaults to `3.5` on the recommendation of Iglewicz and Hoaglin
`return_scores`	optionally, return the modified z score of each observation instead of a masked version of the input vector

Full details are provided in:

Boris Iglewicz and David Hoaglin (1993), "Volume 16: How to Detect and Handle Outliers", The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, ed.

a vector of the same length as the input, with outliers masked (or, if return_scores is true, the modified z scores of each observation)

skinnider/modern documentation built on Feb. 20, 2020, 1:52 p.m.