epMDS {ExPosition} R Documentation

## 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)


### 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.

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.

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)


[Package ExPosition version 2.8.23 Index]