hbrwts_gr: HBR Weight
In rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

Description Usage Arguments Details Author(s) References See Also

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.

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

`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

The ww package explains how it is obtained.

J. W. McKean

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.

GEER_est

rlme documentation built on May 2, 2019, 3:47 p.m.