hbrwts_gr: HBR Weight

In herbps10/rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

HBR Weight

Description

Calculates hbr weights for the GEER method. This turns a vector of weights for a vector of errors. Used to make factor space more robust, up to 50% breakdown. See HM (2012) and Terpstra and McKean (2005) for details. The ww package produces this weights as well.

Usage

hbrwts_gr(xmat, y, percent = 0.95, intest = ltsreg(xmat, y)$coef)

Arguments

xmat	Design matrix, pxn, without intercept.
y	Response vector in nx1.
percent	This is 0.95.
intest	This is obtained from myltsreg(xmat, y)$coef

Details

The ww package explains how it is obtained.

Author(s)

J. W. McKean

References

T. P. Hettmansperger and J. W. McKean. Robust Nonparametric Statistical Methods. Chapman Hall, 2012.

J. T. Terpstra and J. W. McKean. Rank-based analysis of linear models using R. Journal of Statistical Software, 14(7):1 - 26, 7 2005. ISSN 1548-7660. URL http://www.jstatsoft.org/v14/i07.

See Also

GEER_est

herbps10/rlme documentation built on Nov. 25, 2022, 1:38 p.m.