Predict: Estimates of Parameters in Circular-Circular Regression

In CircOutlier: Detection of Outliers 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=α+β x_i+ε_i (mod 2π) (i=1,2,...,n)
y	the response variable on model y_i=α+β x_i+ε_i (mod 2π) (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 α
beta1	estimate of β

.

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)

Example output

Loading required package: CircStats
Loading required package: MASS
Loading required package: boot
Loading required package: circular

Attaching package: 'circular'

The following objects are masked from 'package:CircStats':

    A1, A1inv, I.0, I.1, I.p, deg, plot.edf, pp.plot, rad, rose.diag,
    rstable

The following objects are masked from 'package:stats':

    sd, var


Attaching package: 'CircOutlier'

The following object is masked from 'package:circular':

    wind

$output
      alpha1    beta1
[1,] 6.00976 1.284082

CircOutlier documentation built on May 2, 2019, 6:04 a.m.