DMCEE {CircOutlier}

Detection of Outliers in Circular-Circular Regression

Description

This function calculates the absolute values of the difference between the values of MCE and MCe statistic. Then, it draws the scatter plot of them and estimates the concentration parameter of k. Given the sample size and the estimated value of K, cut-off point from the table DMCE in the significance level of 0.05 or 0.1 will be found. Outliers are locatedon the top of teh line corresponding to the cut-off point.

Usage

DMCEE(x, y, b)

Arguments

x

independent variable on model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi) (i=1,2,...,n)

y

the response variable on model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi) (i=1,2,...,n)

b

the level of significance (0.05 or 0.1)

Details

The ith observation is identified as an outlier if the absolute values of the difference between the values of MCE and MCe statistic exceeds a pre-specified cut-off point.

Author(s)

Azade Ghazanfarihesari, Majid Sarmad

References

A. H. Abuzaid, A. G. Hussin & I. B. Mohamed (2013) Detection of outliers in simple circular regression models using the mean circular error statistics

See Also

circular, CircStats

Examples

data(wind2)
DMCEE(wind2[,1], wind2[,2], .9)