Huberized {CircOutlier}

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)