# MDS: Multidimensional Scaling In provenance: Statistical Toolbox for Sedimentary Provenance Analysis

 MDS R Documentation

## Multidimensional Scaling

### Description

Performs classical or nonmetric Multidimensional Scaling analysis of provenance data

### Usage

``````MDS(x, ...)

## Default S3 method:
MDS(x, classical = FALSE, k = 2, ...)

## S3 method for class 'compositional'
MDS(x, classical = FALSE, k = 2, ...)

## S3 method for class 'counts'
MDS(x, classical = FALSE, k = 2, ...)

## S3 method for class 'distributional'
MDS(x, classical = FALSE, k = 2, nb = 0, ...)

## S3 method for class 'varietal'
MDS(x, classical = FALSE, k = 2, nb = 0, ...)
``````

### Arguments

 `x` an object of class `distributional`, `compositional`, `counts`, `varietal` or `diss` `...` optional arguments If `x` has class `distributional`, `...` is passed on to `diss.distributional`. If `x` has class `compositional`, `...` is passed on to `diss.compositional`. If `x` has class `counts`, `...` is passed on to `diss.counts`. If `x` has class `varietal`, `...` is passed on to `diss.varietal`. Otherwise, `...` is passed on to `cmdscale` (if `classical=TRUE`), to `isoMDS` (if `classical=FALSE`). `classical` boolean flag indicating whether classical (`TRUE`) or nonmetric (`FALSE`) MDS should be used `k` the desired dimensionality of the solution `nb` number of bootstrap resamples. If `nb>0`, then `plot.MDS(...)` will visualise the sampling uncertainty as polygons (inspired by Nordsvan et al. 2020). The bigger `nb`, the slower the calculations. `nb=10` seems a good compromise.

### Value

an object of class `MDS`, i.e. a list containing the following items:

`points`: a two column vector of the fitted configuration

`classical`: a boolean flag indicating whether the MDS configuration was obtained by classical (`TRUE`) or nonmetric (`FALSE`) MDS.

`diss`: the dissimilarity matrix used for the MDS analysis

`stress`: (only if `classical=TRUE`) the final stress achieved (in percent)

### References

Nordsvan, A.R., Kirscher, U., Kirkland, C.L., Barham, M. and Brennan, D.T., 2020. Resampling (detrital) zircon age distributions for accurate multidimensional scaling solutions. Earth-Science Reviews, p.103149.

Vermeesch, P., 2013, Multi-sample comparison of detrital age distributions. Chemical Geology v.341, 140-146, doi:10.1016/j.chemgeo.2013.01.010

### Examples

``````data(Namib)
plot(MDS(Namib\$Major,classical=TRUE))
``````

provenance documentation built on Aug. 28, 2023, 5:07 p.m.