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().
rprmeddis(I, sec, mat, ehat, location, scale, rprpair = "hl-disp")Number of clusters.
A vector of subcluster numbers in clusters.
A matrix of numbers of observations in subclusters. Dimension is Ixmax(number ofsubclusters). Each row indicates one cluster.
The residuals that inherits random effects and error effect to be predicted.
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.
1 means mad, 2 means disp as scale estimators
Character string indicating the location and scale parameters to use. Default to "hl-disp", but may also be "med-mad". See Bilgic (2012).
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.
Y. K. Bilgic. Rank-based estimation and prediction for mixed effects models in nested designs. 2012. URL http://scholarworks.wmich.edu/dissertations/40. Dissertation.