R: Detecting Outliers in Circular Data and Modifying Its
Huberized
R Documentation
Detecting Outliers in Circular Data and Modifying Its
Description
This function is used to identify outliers in circular data sets. and with do the procedure Huberized on this outliers, the results improve. Huberizing the outliers will improve the results. circular and sd.circular are mean and standard deviation of circular data.
Usage
Huberized(t)
Arguments
t
circular data set which contains suspected outliers.
Details
In this method, we progressively transform the original data by a process called winsorisation. Assume that we have initial estimates called m,s. (These coulde evaluated as mean and standard deviation.) If a value x_i falls above
m+(1.5*s) then we change it to x_i=m+(1.5*s).
Likewise if a value falls below m-(1.5*s) then we change it to x_i=m=(1.5*s). We then
calculate an improved estimate of mean as m1=mean.circular(x_i), and of the standard deviation as s1=1.134*(sd.circular(x_i)).(The factor 1.134 is derived
from the normal distribution, given a value 1.5 for the multiplier most often used in the winsorisation process.) (see the first reference)
Value
Two plot and four number
a list containing the following values:
plot1
plot data set when exist outlier.
plot2
plot data set after modified outlier.
m
mean.circular when exist outlier.
m1
mean.circular after modified outlier.
s
sd.circular when exist outlier.
s1
sd.circular after modified outlier.
Author(s)
Azade Ghazanfarihesari, Majid Sarmad
References
Analytical Methods Committe, Robust statistics: a method coping with outliers, Royal Society of Chemistry 2001, amc technical brief.
A. H. Abuzaid, A. G. Hussin & I. B. Mohamed (2013) Detecting of outliers in simple circular regression models
using the mean circular error statistics.
See Also
circular, CircStats
Examples
data(wind)
Huberized(wind)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(CircOutlier)
Loading required package: CircStats
Loading required package: MASS
Loading required package: boot
Loading required package: circular
Attaching package: 'circular'
The following objects are masked from 'package:CircStats':
A1, A1inv, I.0, I.1, I.p, deg, plot.edf, pp.plot, rad, rose.diag,
rstable
The following objects are masked from 'package:stats':
sd, var
Attaching package: 'CircOutlier'
The following object is masked from 'package:circular':
wind
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/CircOutlier/Huberized.Rd_%03d_medium.png", width=480, height=480)
> ### Name: Huberized
> ### Title: Detecting Outliers in Circular Data and Modifying Its
> ### Aliases: Huberized
>
> ### ** Examples
>
> data(wind)
> Huberized(wind)
$output
m s m1 s1
[1,] 0.001709521 0.007761697 0.0009231165 0.00245804
>
>
>
>
>
> dev.off()
null device
1
>