coreMDS {ExPosition} | R Documentation |
coreMDS
Description
coreMDS performs metric multidimensional scaling (MDS).
Usage
coreMDS(DATA, masses = NULL, decomp.approach = 'svd', k = 0)
Arguments
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). |
Details
epMDS
should not be used directly unless you plan on writing
extensions to ExPosition. See epMDS
Value
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...). |
Author(s)
Derek Beaton and Hervé Abdi.
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.