rprmeddis: Rprmeddis

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

Description

Robust rank-based prediction algorithm that gets predictions for random errors in three-level nested design. It needs one location and scale estimators. Hodges-Lehmann location estimate and dispersion functional estimate pair is called with rprpair="hl-disp" -by default- ; median and MAD pair is called with rprpair="med-mad" in rlme().

Usage

rprmeddis(I, sec, mat, ehat, location, scale, 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.
ehat	The residuals that inherits random effects and error effect to be predicted.
location	If location = scale = 1 then use Median and MAD in RPP If location = scale = 2 then use HL & Dispvar in RPP Note: this is deprecated. You should specify the location & scale parameters by using the rprpair parameter.
scale	1 means mad, 2 means disp as scale estimators
rprpair	Character string indicating the location and scale parameters to use. Default to "hl-disp", but may also be "med-mad". See Bilgic (2012).

Details

The rprmeddisp() function yields predictions of random effects and errors vectors along with scale estimates in each level. This function was designed for three-level nested design. See rprmeddisp2() in the package, this is for two-level nested design.

Author(s)

Yusuf Bilgic [e-mail]

References

Y. K. Bilgic. Rank-based estimation and prediction for mixed effects models in nested designs. 2012. URL http://scholarworks.wmich.edu/dissertations/40. Dissertation.

See Also

rpr dispvar

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