JR_est: JR Method

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

Description

Fit a model using the JR method

Usage

JR_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. mat here~~
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 diagnostic messages.

Value

theta	Fixed effect estimates.
ses	Standard error for the fixed esimates.
sigma	Covariate variance estimates using RPP (Groggel and Dubnicka's procedure).
ehat	Raw error.
effect_sch	Cluster random error.
effect_sec	Subcluster random error.
effect_err	Epsilon error.

Author(s)

Yusuf Bilgic

See Also

rlme

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