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Function outlier [GmAMisc v1.1.1]
keywords
outlier
title
R function for univariate outliers detection
description
The function allows to perform univariate outliers detection using three different methods. These methods are those described in: Wilcox R R, "Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy", Springer 2010 (2nd edition), pages 31-35.
Function outlier_detection [mdapack v0.0.2]
keywords
outlier
title
Outlier detection function
description
'outlier_detection'visually detect and highlights outliers in a univariate continuous variable. The function fetches the values of data points that lie beyond the extremes of the whiskers(observations that lie outside of 1.5 * IQR.
Function outliers [FactoInvestigate v1.7]
keywords
outlier
title
Outliers detection
description
Detection of singular individuals that concentrates too much inertia.
Function cook.outliers [referenceIntervals v1.2.0]
keywords
~outlier
title
Determines outliers using Cook's Distance
description
A linear regression model is calculated for the data (which is the mean for one-dimensional data. From that, using the Cook Distances of each data point, outliers are determined and returned.
Function horn.outliers [referenceIntervals v1.2.0]
keywords
~outlier
title
Determines outliers using Horn's method and Tukey's interquartile fences on a Box-Cox transformation of the data.
description
This function determines outliers in a Box-Cox transformed dataset using Horn's method of outlier detection using Tukey's interquartile fences. If a data point lies outside 1.5 * IQR from the 1st or 3rd quartile point, it is an outlier.
Function dixon.outliers [referenceIntervals v1.2.0]
keywords
~outlier
title
Determines outliers using Dixon's Q Test method
description
This determines outliers of the dataset by calculating Dixon's Q statistic and comparing it to a standardized table of statistics. This method can only determine outliers for datasets of size 3 <= n <= 30. This function requires the outliers package.
Function outlier.detection [fdasrvf v1.9.4]
keywords
outlier
title
Outlier Detection
description
This function calculates outlier's using geodesic distances of the SRVFs from the median
Function RSlo [MGBT v1.0.4]
keywords
low outlier (definition)
title
Rosner RST Test Adjusted for Low Outliers
description
The Rosner (1975) method or the essence of the method, given the order statistics \(x_{[1:n]} \le x_{[2:n]} \le \cdots \le x_{[(n-1):n]} \le x_{[n:n]}\), is the statistic: $$RS_r = \frac{ x_{[r:n]} - \mathrm{mean}\{x_{[(r+1)\rightarrow(n-r):n]}\} } {\sqrt{\mathrm{var}\{x_{[(r+1)\rightarrow(n-r):n]}\}}}\mbox{,} $$
Function BLlo [MGBT v1.0.4]
keywords
low outlier (definition)
title
Barnett and Lewis Test Adjusted for Low Outliers
description
The Barnett and Lewis (1995, p. 224; \(T_{\mathrm{N}3}\)) so-labeled “N3 method” with TAC adjustment to look for low outliers. The essence of the method, given the order statistics \(x_{[1:n]} \le x_{[2:n]} \le \cdots \le x_{[(n-1):n]} \le x_{[n:n]}\), is the statistic $$BL_r = T_{\mathrm{N}3} = \frac{ \sum_{i=1}^r x_{[i:n]} - r \times \mathrm{mean}\{x_{[1:n]}\} } {\sqrt{\mathrm{var}\{x_{[1:n]}\}}}\mbox{,}$$ for the mean and variance of the observations. Barnett and Lewis (1995, p. 218) brand this statistic as a test of the “\(k \ge 2\) upper outliers” but for the MGBT package “lower” applies in TAC reformulation. Barnett and Lewis (1995, p. 218) show an example of a modification for two low outliers as \((2\overline{x} - x_{[2:n]} - x_{[1:n]})/s\) for the mean \(\mu\) and standard deviation \(s\). TAC reformulation thus differs by a sign. The \(BL_r\) is a sum of internally studentized deviations from the mean: $$SP(t) \le {n \choose k} P\biggl(\bm{t}(n-2) > \biggr[\frac{n(n-2)t^2}{r(n-r)(n-1)-nt^2}\biggl]^{1/2}\biggr)\mbox{,}$$ where \(\bm{t}(df)\) is the t-distribution for \(df\) degrees of freedom, and this is an inequality when $$t \ge \sqrt{r^2(n-1)(n-r-1)/(nr+n)}\mbox{,}$$ where \(SP(t)\) is the probability that \(T_{\mathrm{N}3} > t\) when the inequality holds. For reference, Barnett and Lewis (1995, p. 491) example tables of critical values for \(n=10\) for \(k \in 2,3,4\) at 5-percent significant level are \(3.18\), \(3.82\), and \(4.17\), respectively. One of these is evaluated in the Examples.
Function shape.fd.outliers [ddalpha v1.3.11]
keywords
outlier
title
Functional Depth-Based Shape Outlier Detection
description
Detects functional outliers of first three orders, based on the order extended integrated depth for functional data.