epMDS function

epMDS: Multidimensional Scaling (MDS) via ExPosition.

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)

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.

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.

Details

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

Returns

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.

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.

Author(s)

Derek Beaton

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)