Description Usage Arguments Value Author(s) References See Also Examples
Given a nxn dissimilarity matrix D and a n-vector of binary (1,2) class labels y, this function outputs a set of configuration points z1,...,zn, each a S-vector, such that the distances between the configuration points approximate the dissimilarity matrix D, AND such that zis >= zjs tends to occur when yi >= yj.
1 | TrainSuperMDS(d = NULL, y, alpha = 0.5, S = 2, x = NULL, nstarts = 5, silent = FALSE)
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d |
A nxn dissimilarity matrix. If NULL, then x, a nxp data matrix, must be input instead. |
y |
A n-vector of binary labels, in the form of 1's and 2's. For instance, c(1,1,1,2,2) could be input if D is a 5x5 matrix. |
alpha |
A scalar between 0 and 1. If alpha=0 then this is just least squares MDS, and if alpha=1 then it's completely supervised. |
S |
The number of dimensions of the configuration points z1,...,zn. Must be at least equal to 1. |
x |
A nxp data matrix, to be input only if D is NULL. |
nstarts |
The supervised MDS algorithm finds a local minimum for the objective. Here, specify the number of initial values to try. If nstarts>1 then the set of configuration points corresponding to the optimal (smallest) value of the objective will be reported. |
silent |
Set to TRUE in order to turn off printing output to screen. |
z |
A nxS matrix of the configuration points obtained. |
crits |
The values of the criterion obtained at the iterations of the algorithm. |
stress |
The portion of the final criterion value that are due to the STRESS component of the objective function. |
super |
The portion of the final criterion value that are due to the SUPERVISED component of the objective function. |
Daniela M Witten
Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis.
1 | # Try ?superMDS for examples
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