`MappingQuality()`

calculates the trustworthiness and continuity
of mapped distances (Venna and Kaski 2001; Kaski et al. 2003)
.
Trustworthiness measures, on a scale from 0--1,
the degree to which points that are nearby in a mapping are truly close
neighbours; continuity, the extent to which points that are truly nearby
retain their close spatial proximity in a mapping.

```
MappingQuality(original, mapped, neighbours = 10L)
ProjectionQuality(original, mapped, neighbours = 10L)
```

- original, mapped
Square matrix or

`dist`

object containing original / mapped pairwise distances.- neighbours
Integer specifying number of nearest neighbours to use in calculation. This should typically be small relative to the number of points.

`MappingQuality()`

returns a named vector of length four,
containing the entries: `Trustworthiness`

, `Continuity`

, `TxC`

(the product of these values), and `sqrtTxC`

(its square root).

Kaski S, Nikkila J, Oja M, Venna J, Toronen P, Castren E (2003).
“Trustworthiness and metrics in visualizing similarity of gene expression.”
*BMC Bioinformatics*, **4**, 48.
doi:10.1186/1471-2105-4-48
.

Venna J, Kaski S (2001).
“Neighborhood preservation in nonlinear projection methods: an experimental study.”
In Dorffner G, Bischof H, Hornik K (eds.), *Artificial Neural Networks --- ICANN 2001*, Lecture Notes in Computer Science, 485--491.
doi:10.1007/3-540-44668-0_68
.

Other tree space functions:
`MSTSegments()`

,
`MapTrees()`

,
`SpectralEigens()`

,
`cluster-statistics`

,
`median.multiPhylo()`

```
library("TreeTools", quietly = TRUE)
trees <- as.phylo(0:10, nTip = 8)
distances <- ClusteringInfoDistance(trees)
mapping <- cmdscale(distances)
MappingQuality(distances, dist(mapping), 4)
#> Trustworthiness Continuity TxC sqrtTxC
#> 0.7929293 0.8737374 0.6928120 0.8323533
```