MCe: Detecting of Outliers in Circular Regression

Description

Removal of the ith observation from the data set calculate mean circular error for reduced data set

Usage

MCe(u)

Arguments

cosine the difference between the observed value of the response variable y and fitted values Y on model $y_i=\alpha+\beta x_i+\epsilon_i$(mod 2$\pi$) (i=1,2,...,n).

Value

Number, that is mean circular error after removal of the ith observation from the data set.

Details

This function after removal of the ith observation from the data set.

References

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

Examples

Run this code

# Generate a data set dependent of circular variables.
library(CircStats)
 x <- rvm(n = 50, 0, 2)
y <- rvm(n = 50, pi/4, 5)
# Fit a circular-circular regression model.
circ.lm <- circ.reg(y, x, order = 1)
Y <- circ.lm$fitted
MCe(cos(y - Y))

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