wilstep: Wilcoxon One Step Rank-based Estimate in GR Method

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

Description

Gets weighted rank based fittings for nested designs.

Usage

wilstep(I, sec, mat, init = F, y, x, sigmaa2 = 1, sigmaw2 = 1,
  sigmae2 = 1, thetaold = c(0), eps = 1e-04, iflag2 = 0,
  rprpair = "hl-disp")

Arguments

I	Number of clusters.
sec	A vector of subcluster numbers in clusters.
mat	A matrix of numbers of observations in subclusters. Dimension is Ixmax(number ofsubclusters). Each row indicates one cluster.
init	boolean
y	Response vector of nx1.
x	Design matrix, pxn, without intercept.
sigmaa2	Initial sigma for cluster in three-level design.
sigmaw2	Initial sigma for subcluster in three-level design.
sigmae2	Initial sigma for error in three-level design.
thetaold	Initial input.
eps	Epsilon value
iflag2	y or n
rprpair	Either 'hl-disp' or 'med-mad'

Details

Initial inputs are from the independent model.

Author(s)

J. W. McKean and Y. K. Bilgic

References

Y. K. Bilgic and J. W. McKean. Iteratively reweighted generalized rank-based method in mixed models. 2013. Under preperation.

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.

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