CircOutlier (version 3.1.3)

DMCE: Detecting of Outliers in Circular Regression

Description

Maximum absolute difference between the values of the mean circular statistics for full and reduced data sets

Usage

DMCE(y, Y, n)

Arguments

y

observed values of the response variable are calculated based on model $y_i=\alpha+\beta x_i+\epsilon_i$(mod 2$\pi$) (i=1,2,...,n). here n is sample size. random error having a VonMises distribution with circular mean 0 and concentration

Y

the estimated value of y under model $y_i=\alpha+\beta x_i+\epsilon_i$(mod 2$\pi$) (i=1,2,...,n).

n

the sample size

Value

• Number, that is Maximum absolute difference between the values of the mean circular statistics for full and reduced data sets.

Details

The ith observation is identified as an outlier if the difference of means circular error (DMCE) exceeds a pre-specified cut-off point.

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

See Also

circular, CircStats

Examples

# Generate a data set dependent of circular variables.
library(CircStats)
n <- 50
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
DMCE(y, Y, n)