Predict {CircOutlier}

Estimates of Parameters in Circular-Circular Regression

Description

This function calculated the maximum-likelihood estimates parameters

Usage

Predict(x, y)

Arguments

x

independent variable on model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi) (i=1,2,...,n)

y

the response variable on model y_i=\alpha+\beta x_i+\epsilon_i (mod 2\pi) (i=1,2,...,n)

Details

This function uses of iterative methods for the parameter estimates in circular-circular regression model and The user can default values The desired change.

Value

Number

a list containing the following values:

alpha1

estimate of \alpha

beta1

estimate of \beta

.

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)
Predict(x, y)