|
Print DistatisR results.
● Data Source:
CranContrib
● Keywords: print
● Alias: print.DistatisR
●
0 images
|
|
Print S+ matrix results.
● Data Source:
CranContrib
● Keywords: print
● Alias: print.Splus
●
0 images
|
|
Print C matrix results.
● Data Source:
CranContrib
● Keywords: print
● Alias: print.Cmat
●
0 images
|
mmds
(Package: DistatisR) :
mmds Metric (classical) Multidimensional Scaling (a.k.a Principal Coordinate Analysis) of a (Euclidean) Distance Matric
Perform an MMDS of a (Euclidean) distance matrix measured between a set of weighted objects.
● Data Source:
CranContrib
● Keywords: DistatisR
● Alias: mmds
●
0 images
|
Implements the DISTATIS method which a 3-way generalization of metric multidimensional scaling (a.k.a. classical MDS or principal coordinate analysis). distatis takes a set of K distance matrices describing a set of I observations and computes (1) a set of factor scores that describes the similarity structure of the distance matrices (e.g., what distance matrices describe the observations in the same way, what distance matrices differ from each other) (2) a set of factor scores (called the compromise factor scores) for the observations that best describes the similarity structure of the observations and (3) partial factor scores that show how each individual distance matrix "sees" the compromise space. distatis computes the compromise as an optimum linear combination of the cross-product matrices associated to each distance matrix. distatis can also be applied to a set of covariance matrices.
● Data Source:
CranContrib
● Keywords: distatis, mds
● Alias: CovSTATIS, distatis
●
0 images
|
|
Plot maps of the factor scores of the observations for a distatis analysis. The factor scores are obtained from the eigen-decomposition of the between distance matrices cosine matrix (often a matrix of Rv coefficients). Note that the factor scores for the first dimension are always positive. There are used to derive the alpha weights for DISTATIS.
● Data Source:
CranContrib
● Keywords: DistatisR
● Alias: GraphDistatisRv
●
0 images
|
GraphDistatisPartial plots maps of the factor scores of the observations from a distatis analysis. GraphDistatisPartial gives a map of the factors scores of the observations plus partial factor scores, as "seen" by each of the matrices.
● Data Source:
CranContrib
● Keywords: DistatisR, mds
● Alias: GraphDistatisPartial
●
0 images
|
Plot maps of the factor scores of the observations for a distatis analysis. GraphDistatis gives a map of the factor scores for the observations. The labels of the observations are plotted by defaults but can be omitted (see the nude=TRUE option).
● Data Source:
CranContrib
● Keywords: DistatisR, mds
● Alias: GraphDistatisCompromise
●
0 images
|
GraphDistatisBoot plots maps of the factor scores of the observations from a distatis analysis. GraphDistatisBoot gives a map of the factors scores of the observations plus the boostrapped confidence intervals drawn as "Confidence Ellipsoids" at percentage%.
● Data Source:
CranContrib
● Keywords: DistatisR
● Alias: GraphDistatisBoot
●
0 images
|
|
This function produces 4 plots: (1) a compromise plot, (2) a partial factor scores plot, (3) a bootstrap confidence intervals plot, and (4) a Rv map.
● Data Source:
CranContrib
● Keywords: distatis, mds
● Alias: GraphDistatisAll
●
0 images
|
providing 
.