jaeckel | R Documentation |
Uses the built-in function optim
to minimize Jaeckel's dispersion function with respect to beta.
jaeckel(x, y, beta0 = lm(y ~ x)$coef[2:(ncol(x) + 1)],
scores = Rfit::wscores, control = NULL,...)
x |
n by p design matrix |
y |
n by 1 response vector |
beta0 |
initial estimate of beta |
scores |
object of class 'scores' |
control |
control passed to fitting routine |
... |
addtional arguments to be passed to fitting routine |
Jaeckel's dispersion function (Jaeckel 1972) is a convex function which measures the distance between the observed responses y
and the fitted values x \beta
.
The dispersion function is a sum of the products of the residuals, y - x \beta
, and the scored ranks of the residuals. A rank-based fit minimizes the dispersion function; see McKean and Schrader (1980) and Kloke and McKean (2012) for discussion.
jaeckel
uses optim
with the method set to BFGS to minimize Jaeckel's dispersion function.
If control is not specified at the function call, the relative tolerance (reltol) is set to .Machine$double.eps^(3/4)
and maximum number of iterations is set to 200.
jaeckel
is intended to be an internal function. See rfit
for a general purpose function.
Results of optim
are returned.
John Kloke
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.
Kapenga, J. A., McKean, J. W., and Vidmar, T. J. (1988), RGLM: Users Manual, Statist. Assoc. Short Course on Robust Statistical Procedures for the Analysis of Linear and Nonlinear Models, New Orleans.
optim
, rfit
## This is a internal function. See rfit for user-level examples.
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