smacof (version 1.8-13)

smacofRect: Rectangular smacof

Description

Variant of smacof for rectangular matrices (typically ratings, preferences) which is also known as metric unfolding.

Usage

smacofRect(delta, ndim = 2, circle = c("none","row","column"), weightmat = NULL,  init = NULL, verbose = FALSE, itmax = 1000, reg = 1e-6, eps = 1e-6) 
unfolding(delta, ndim = 2, circle = c("none","row","column"), weightmat = NULL,  init = NULL, verbose = FALSE, itmax = 1000, reg = 1e-6, eps = 1e-6)

Arguments

delta

Data frame or matrix of preferences, ratings, dissimilarities.

ndim

Number of dimensions

circle

If "column", the column configurations are restricted to be on a circle, if "row", row configurations are on a circle, if "none", there are no restrictions on row and column configurations

weightmat

Optional matrix with dissimilarity weights

init

Matrix with starting values for configurations (optional)

verbose

If TRUE, intermediate stress is printed out

itmax

Maximum number of iterations

reg

Regularization factor, prevents distances from being 0

eps

Convergence criterion

Value

obsdiss

Observed dissimilarities, corresponds to delta

confdiss

Configuration dissimilarities

conf.row

Matrix of final row configurations

conf.col

Matrix of final column configurations

stress

Final, normalized stress value

spp.row

Stress per point, rows

spp.col

Stress per point, columns

congvec

Vector of congruency coefficients

ndim

Number of dimensions

model

Type of smacof model

niter

Number of iterations

nind

Number of individuals (rows)

nobj

Number of objects (columns)

Details

Creates an object of class smacofR. The unfolding() function is a wrapper function and can be used instead of smacofRect().

References

de Leeuw, J. & Mair, P. (2009). Multidimensional scaling using majorization: The R package smacof. Journal of Statistical Software, 31(3), 1-30, http://www.jstatsoft.org/v31/i03/

See Also

smacofConstraint, smacofSym, smacofIndDiff, smacofSphere

Examples

res <- unfolding(breakfast)
res
summary(res)

## various configuration plots
plot(res)
plot(res, type = "p", pch = 25)
plot(res, type = "p", pch = 25, col.columns = 3, 
label.conf.columns = list(label = TRUE, pos = 3, col = 3), 
col.rows = 8, label.conf.rows = list(label = TRUE, pos = 3, col = 8))