ExPosition (version 2.8.23)

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

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)

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.

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.

Author

Derek Beaton

Details

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

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)