DMCEE: Detection of Outliers in Circular-Circular Regression
In CircOutlier: Detection of Outliers in Circular-Circular Regression

Description Usage Arguments Details Author(s) References See Also Examples

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.

1	DMCEE(x, y, b)

`x`	independent variable on model y_i=α+β x_i+ε_i (mod 2π) (i=1,2,...,n)
`y`	the response variable on model y_i=α+β x_i+ε_i (mod 2π) (i=1,2,...,n)
`b`	the level of significance (0.05 or 0.1)

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.

Azade Ghazanfarihesari, Majid Sarmad

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

circular, CircStats

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

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, 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