MCe {CircOutlier}

Detection of Outliers in Circular-circular Regression

Description

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

Usage

MCe(u)

Arguments

u

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).

Details

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

Value

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

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

# 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(x, y, order = 1)
Y <- circ.lm$fitted
MCe(cos(y - Y))