grad function

Calculate the Gradiant of Jaeckel's Dispersion Function

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

Returns

The gradiant evaluated at beta.

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.

Author(s)

John Kloke

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)
  }