epMDS: epMDS: Multidimensional Scaling (MDS) via ExPosition.

In ExPosition: Exploratory Analysis with the Singular Value Decomposition

Description

Multidimensional Scaling (MDS) via ExPosition.

Usage

epMDS(DATA, DATA_is_dist = TRUE, method="euclidean", DESIGN = NULL, 
	make_design_nominal = TRUE, masses = NULL, graphs = TRUE, k = 0)

Arguments

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. method matches dist; Two additional distances are avaialble: "correlation" and "chi2". For "chi2" see chi2Dist. Default is "euclidean".
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 epGraphs)
k	number of components to return.

Details

epMDS performs metric multi-dimensional scaling. Essentially, a PCA for a symmetric distance matrix.

Value

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.

Note

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.

Author(s)

Derek Beaton

References

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.

See Also

corePCA, epPCA, epGPCA

Examples

	
	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)

ExPosition documentation built on May 1, 2019, 7:06 p.m.