Description Usage Arguments Value References Examples

Given a distance matrix and a valid MDS representation for it, improve the R-square correlation between observed and approximated distances until converged is reached for a given threshold.

1 2 |

`D` |
Distance matrix. |

`Y` |
Matrix with points from a valid MDS solution for the distances in D. |

`rate` |
Grid step rate, start with 0.1 which usually is a good compromise, try also 0.01, 1, 10. |

`maxit` |
Maximum number of iterations. |

`tol` |
Tolerace for R-square convergence. |

`samplesize` |
When there are over 100 points to represent, the gradiend descent step size is determined
using a fraction |

`verbose` |
Give details of the gains in R-square and step size. |

`scale` |
Whether to scale the MDS coordinates in the output MDS. |

`seed` |
A random seed to be used in the resampling process if samplesize < 1. |

`plt` |
Whether to plot the intermediate solutions or not. |

`mc.cores` |
Number of cores to use in parallelized grid step size search. |

The function returns a matrix with the coordinates of a valid MDS solution for distance matrix D where the R-square correlation has been improved. However, have in mind that an MDS solution with better R-square does not necessarily mean the solution is easier to interpret. As with any MDS approach, a balance must be found between pure 'technical' goodness-of-fit and usefulness of the delivered solution in terms of answering the original hypothesis.

boostMDS is based on hitMDS (High-Throughput Multidimensional Scaling, see see http://dig.ipk-gatersleben.de/hitmds/hitmds.html for details)

1 2 3 4 5 6 7 8 | ```
# Not run, see also chroGPS-manual.pdf file for examples
#data(geneSample)
#d = distGPS(geneSample,uniqueRows=TRUE)
#m = mds(d,type='isoMDS')
#m
#plot(m)
#m = boostMDS([email protected],[email protected])
#plot(m)
``` |

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