View source: R/trustworthiness.R

calcTrustworthinessFromRank | R Documentation |

The trustworthiness was proposed by Venna and Kaski, as a local quality
measure of a low-dimensional representation. The metric focuses on the
preservation of local neighborhoods, and compares the neighborhoods of points
in the low-dimensional representation to those in the reference data. Hence,
the trustworthiness measure indicates to which degree we can trust that the
points placed closest to a given sample in the low-dimensional representation
are really close to the sample also in the reference data set. The `kTM`

parameter defines the size of the neighborhoods to consider.

```
calcTrustworthinessFromRank(rankReference, rankLowDim, kTM)
```

`rankReference` |
N x N matrix, each row/column corresponding to one sample. The value of entry (i, j) represents the position of sample i in the ranking of all samples with respect to their distance from sample j, based on the reference (high-dimensional) observed values. The sample itself has rank 0. |

`rankLowDim` |
N x N matrix, each row/column corresponding to one sample. The value of entry (i, j) represents the position of sample i in the ranking of all samples with respect to their distance from sample j, based on the low-dimensional representation. The sample itself has rank 0. |

`kTM` |
The number of nearest neighbors. |

The trustworthiness value.

Charlotte Soneson

Venna J., Kaski S. (2001). Neighborhood preservation in nonlinear projection methods: An experimental study. In Dorffner G., Bischof H., Hornik K., editors, Proceedings of ICANN 2001, pp 485–491. Springer, Berlin.

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