epMDS {ExPosition} | R Documentation |
Multidimensional Scaling (MDS) via ExPosition.
epMDS(DATA, DATA_is_dist = TRUE, method="euclidean", DESIGN = NULL,
make_design_nominal = TRUE, masses = NULL, graphs = TRUE, k = 0)
DATA |
original data to perform a MDS on. |
DATA_is_dist |
a boolean. If TRUE (default) the DATA matrix should be a symmetric distance matrix. If FALSE, a Euclidean distance of row items will be computed and used. |
method |
which distance metric should be used. |
DESIGN |
a design matrix to indicate if rows belong to groups. |
make_design_nominal |
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix. |
masses |
a diagonal matrix (or vector) that contains the masses (for the row items). |
graphs |
a boolean. If TRUE (default), graphs and plots are provided (via |
k |
number of components to return. |
epMDS
performs metric multi-dimensional scaling. Essentially, a PCA for a symmetric distance matrix.
See coreMDS
for details on what is returned. epMDS only returns values related to row items (e.g., fi, ci); no column data is returned.
D |
the distance matrix that was decomposed. In most cases, it is returned as a squared distance. |
With respect to input of DATA
, epMDS
differs slightly from other versions of multi-dimensional scaling.
If you provide a rectangular matrix (e.g., observations x measures), epMDS
will compute a distance matrix and square it.
If you provide a distance (dissimilarity) matrix, epMDS
does not square it.
Derek Beaton
Abdi, H. (2007). Metric multidimensional scaling. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 598-605.
O'Toole, A. J., Jiang, F., Abdi, H., and Haxby, J. V. (2005). Partially distributed representations of objects and faces in ventral temporal cortex. Journal of Cognitive Neuroscience, 17(4), 580-590.
data(jocn.2005.fmri)
#by default, components 1 and 2 will be plotted.
mds.res.images <- epMDS(jocn.2005.fmri$images$data)
##iris example
data(ep.iris)
iris.rectangular <- epMDS(ep.iris$data,DATA_is_dist=FALSE)
iris.euc.dist <- dist(ep.iris$data,upper=TRUE,diag=TRUE)
iris.sq.euc.dist <- as.matrix(iris.euc.dist^2)
iris.sq <- epMDS(iris.sq.euc.dist)