mce_1: Detection of Outliers in Circular-circular Regression
In CircOutlier: Detection of Outliers in Circular-Circular Regression

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

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

MCe(u)

`u`	cosine the difference between the observed value of the response variable y and fitted values Y on model y_i=α+β x_i+ε_i(mod 2π) (i=1,2,...,n).

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

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

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

# 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))