GR_est: GR Method

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

GR Method

Description

Fits a model using the GR method

Usage

GR_est(x, y, I, sec, mat, school, section, rprpair = "hl-disp",
  verbose = FALSE)

Arguments

x	Covariate matrix or data frame.
y	Response matrix or data frame.
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.
school	A vector of clusters, nx1.
section	A vector of subclusters, nx1.
rprpair	By default, it uses "hl-disp" in the random prediction procedure (RPP). Also, "med-mad" would be an alternative.
verbose	Boolean indicating whether to print out messages from the algorithm.

Value

theta	Fixed effect estimates.
ses	Standard error for the fixed esimates.
sigma	Variances of cluster, subcluster, and residual.
ehat	Raw error.
ehats	Independence error from last weighted step.
effect_sch	Cluster random error.
effect_sec	Subcluster random error.
effect_err	Epsilon error.

Author(s)

Yusuf Bilgic

Examples

# See rlme function

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