mdpref | R Documentation |
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.
mdpref(dset,rank.vector=FALSE,ndim=2)
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. |
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.
item |
Coordinates of the items. |
ranking |
Coordinates of the rankings. |
explain |
Proportion of variance explained by the number of dimensions specified. |
Paul H. Lee and Philip L. H. Yu
Carroll, J. D. (1972) Individual differences and multidimensional scaling. In Shepard, R. N., Ronney, A. K., and Nerlove, S. B. (eds.)
## 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)
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