coreMDS {ExPosition} | R Documentation |
coreMDS performs metric multidimensional scaling (MDS).
coreMDS(DATA, masses = NULL, decomp.approach = 'svd', k = 0)
DATA |
original data to decompose and analyze via the singular value decomposition. |
masses |
a vector or diagonal matrix with masses for the rows (observations). If NULL, one is created. |
decomp.approach |
string. A switch for different decompositions (typically for speed). See |
k |
number of components to return (this is not a rotation, just an a priori selection of how much data should be returned). |
epMDS
should not be used directly unless you plan on writing extensions to ExPosition. See epMDS
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.
fi |
factor scores for the row items. |
di |
square distances of the row items. |
ci |
contributions (to the variance) of the row items. |
ri |
cosines of the row items. |
masses |
a column-vector or diagonal matrix of masses (for the rows) |
t |
the percent of explained variance per component (tau). |
eigs |
the eigenvalues from the decomposition. |
pdq |
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. |
X |
the final matrix that was decomposed (includes scaling, centering, masses, etc...). |
Derek Beaton and HervĂ© Abdi.
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.