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smacofSym {smacof} | R Documentation |

Multidimensional scaling (stress minimization: SMACOF) on symmetric dissimilarity matrix.

smacofSym(delta, ndim = 2, type = c("ratio", "interval", "ordinal", "mspline"), weightmat = NULL, init = NULL, ties = "primary", verbose = FALSE, relax = FALSE, modulus = 1, itmax = 1000, eps = 1e-06, spline.degree = 2, spline.intKnots = 2)

`delta` |
Either a symmetric dissimilarity matrix or an object of class |

`ndim` |
Number of dimensions |

`weightmat` |
Optional matrix with dissimilarity weights |

`init` |
Matrix with starting values for configurations (optional) |

`type` |
MDS type: |

`ties` |
Tie specification for ordinal MDS only: |

`verbose` |
If |

`relax` |
If |

`modulus` |
Number of smacof iterations per monotone regression call |

`itmax` |
Maximum number of iterations |

`eps` |
Convergence criterion |

`spline.degree` |
Degree of the spline for |

`spline.intKnots` |
Number of interior knots of the spline for |

This is the simplest MDS-SMACOF version of the package. It solves the
stress target function for symmetric dissimiliby means of the
majorization approach (SMACOF) and reports the Stress-1 value
(normalized). The main output are the coordinates in the low-dimensional
space (configurations; `conf`

).
This function allows for fitting three basic types of MDS: ratio MDS
(default), interval MDS (polynomial transformation), and ordinal MDS
(aka nonmetric MDS).
It also returns the point stress, i.e. the larger the contribution of a
point to the total stress, the worse the fit (see also `plot.smacof`

.

`delta` |
Observed dissimilarities, not normalized |

`obsdiss` |
Observed dissimilarities, normalized |

`confdiss` |
Configuration dissimilarities |

`conf` |
Matrix of fitted configurations |

`stress` |
Stress-1 value for metric MDS |

`spp` |
Stress per point |

`ndim` |
Number of dimensions |

`model` |
Name of smacof model |

`niter` |
Number of iterations |

`nobj` |
Number of objects |

`type` |
Type of MDS model |

Jan de Leeuw and Patrick Mair

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/

Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling (2nd ed.). Springer.

Borg, I., Groenen, P. J. F., & Mair, P. (2013). Applied Multidimensional Scaling. Springer.

`smacofConstraint`

, `smacofRect`

, `smacofIndDiff`

, `smacofSphere`

, `plot.smacof`

## simple SMACOF solution for kinship data data(kinshipdelta) res <- smacofSym(kinshipdelta) res summary(res) plot(res) plot(res, type = "p", label.conf = list(TRUE, 3, "darkgray"), pch = 25, col = "red") ## interval MDS res <- smacofSym(kinshipdelta, type = "interval") res ## 3D nonmetric SMACOF solution for trading data (secondary approach to ties) data(trading) res <- smacofSym(trading, ndim = 3, type = "ordinal", ties = "secondary") res

[Package *smacof* version 1.5-0 Index]