coreMDS performs metric multidimensional scaling (MDS).
coreMDS(DATA, masses = NULL, decomp.approach = 'svd', k = 0)original data to decompose and analyze via the singular value decomposition.
a vector or diagonal matrix with masses for the rows (observations). If NULL, one is created.
string. A switch for different decompositions (typically for speed). See pickSVD.
number of components to return (this is not a rotation, just an a priori selection of how much data should be returned).
Returns a large list of items which are also returned in epMDS.
All items with a letter followed by an i are for the I rows of a DATA matrix. All items with a letter followed by an j are for the J rows of a DATA matrix.
factor scores for the row items.
square distances of the row items.
contributions (to the variance) of the row items.
cosines of the row items.
a column-vector or diagonal matrix of masses (for the rows)
the percent of explained variance per component (tau).
the eigenvalues from the decomposition.
the set of left singular vectors (pdq$p) for the rows, singular values (pdq$Dv and pdq$Dd), and the set of right singular vectors (pdq$q) for the columns.
the final matrix that was decomposed (includes scaling, centering, masses, etc...).
epMDS should not be used directly unless you plan on writing extensions to ExPosition. See epMDS
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.