grad: Calculate the Gradiant of Jaeckel's Dispersion Function

In kloke/Rfit: Rank-Based Estimation for Linear Models

Calculate the Gradiant of Jaeckel's Dispersion Function

Description

Calculate the Gradiant of Jaeckel's Dispersion Function

Usage

grad(x, y, beta, scores)

Arguments

x	n by p design matrix
y	n by 1 response vector
beta	p by 1 vector of regression coefficients
scores	an object of class scores

Value

The gradiant evaluated at beta.

Author(s)

John Kloke

References

Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.

Jaeckel, L. A. (1972). Estimating regression coefficients by minimizing the dispersion of residuals. Annals of Mathematical Statistics, 43, 1449 - 1458.

Jureckova, J. (1971). Nonparametric estimate of regression coefficients. Annals of Mathematical Statistics, 42, 1328 - 1338.

See Also

disp

Examples

## The function is currently defined as
function (x, y, beta, scores) 
{
    x <- as.matrix(x)
    e <- y - x %*% beta
    r <- rank(e, ties.method = "first")/(length(e) + 1)
    -t(x) %*% scores@phi(r)
  }

kloke/Rfit documentation built on June 3, 2024, 8:25 a.m.