| mdpref {pmr} | R Documentation |
Multidimensional preference analysis.
Description
Display a 2D plot of the position of both judges and items. The items are labeled with consecutive numbers 1, 2, ..., k while the judges are presented as vectors pointing from the origin to their most preferred items.
Usage
mdpref(dset,rank.vector=FALSE,ndim=2)
Arguments
dset |
a ranking dataset |
rank.vector |
The vectors of the rankings at default will be displayed if the value is set to TRUE. |
ndim |
The number of dimensions extracted from the singular value decomposition. |
Details
Multidimenional preference analysis is a dimension reduction technique which aims to project the high-dimensional ranking data into 2D or 3D plot. Dimension reduction is done using singular value decomposition. Note that the perpendicular projection of the item points onto a judge vector represents the ranking of these items by this judge.
Value
item |
Coordinates of the items. |
ranking |
Coordinates of the rankings. |
explain |
Proportion of variance explained by the number of dimensions specified. |
Author(s)
Paul H. Lee and Philip L. H. Yu
References
Carroll, J. D. (1972) Individual differences and multidimensional scaling. In Shepard, R. N., Ronney, A. K., and Nerlove, S. B. (eds.)
Examples
## create an artificial dataset
X1 <- c(1,1,2,2,3,3)
X2 <- c(2,3,1,3,1,2)
X3 <- c(3,2,3,1,2,1)
n <- c(6,5,4,3,2,1)
test <- data.frame(X1,X2,X3,n)
## multidimensional preference analysis of the artificial dataset
## mdpref(test,rank.vector=TRUE)