plot.acomp {compositions} | R Documentation

## Displaying compositions in ternary diagrams

### Description

### Usage

     ## S3 method for class 'acomp':
     plot(x,...,labels=colnames(X),cn=colnames(X),
              aspanel=FALSE,id=FALSE,idlabs=NULL,idcol=2,center=FALSE,
              scale=FALSE,pca=FALSE,col.pca=par("col"),margin="acomp",
              add=FALSE,triangle=!add,col=par("col"))
     ## S3 method for class 'rcomp':
     plot(x,...,labels=colnames(X),cn=colnames(X),
              aspanel=FALSE,id=FALSE,idlabs=NULL,idcol=2,center=FALSE,
              scale=FALSE,pca=FALSE,col.pca=par("col"),margin="rcomp",
              add=FALSE,col=par("col"))

### Arguments

`x` | a dataset of a compositional class
`...` | further graphical parameters passed (see `par`)
`margin` | the type of marginalisation to be computed, when displaying the individual panels. Possible values are: `"acomp"`, `"rcomp"` and any of the variable names/column numbers in the composition. If one of the columns is selected each panel displays a subcomposition given by the row part, the column part and the given part. If one of the classes is given the corresponding margin `acompmargin` or `rcompmargin` is used.
`add` | a logical indicating whether the information should just be added to an existing plot. If FALSE a new plot is created
`triangle` | a logical indicating whether the triangle should be drawn
`col` | the color to plot the data
`labels` | the names of the parts
`cn` | the names of the parts to be used in a single panel. Internal use only.
`aspanel` | logical indicating that only a single panel should be drawn and not the whole plot. Internal use only
`id` | logical, if TRUE one can identify the points like with the `identify` command.
`idlabs` | a character vector providing the labels to be used with the identification, when `id=TRUE`
`idcol` | color of the `idlabs` labels
`center` | a logical indicating whether a the data should be centered prior to the plot. Centering is done in the choosen geometry. See `scale`
`scale` | a logical indicating whether a the data should be scaled prior to the plot. Scaling is done in the choosen geometry. See `scale`
`pca` | a logical indicating whether the first principal component should be displayed in the plot. Currently, the direction of the principal component of the displayed subcomposition is displayed as a line. In a future, the projected principal componenent of the whole dataset should be displayed.
`col.pca` | The color to draw the principal component.

### Details

The data is displayed in ternary diagrams. Thus, it does not work for two-part compositions. Compositions of three parts are displayed in a single ternary diagram. For compositions of more than three components, the data is arranged in a scatterplot matrix through the command `pairs`.
In this case, the third component in each of the panels is chosen according to setting of `margin=`. Possible values of `margin=` are: `"acomp"`, `"rcomp"` and any of the variable names/column numbers in the composition. If one of the columns is selected each panel displays a subcomposition given by the row part, the column part and the given part. If one of the classes is given the corresponding margin `acompmargin` or `rcompmargin` is used.
Ternary diagrams can be read in multiple ways. Each corner of the triangle corresponds to an extreme composition containing only the part displayed in that corner. Points on the edges correspond to compositions containing only the parts in the adjacent corners. The relative amounts are displayed by the distance to the opposite corner (so-called barycentric coordinates). The individual portions of any point can be infered by drawing a line through the investigated point, and parallel to the edge opposite to the corner of the part of interest. The portion of this part is constant along the line. Thus we can read it on the sides of the ternary diagram, where the line crosses its borders. Note that these `isoPortionLines` remain straight under an arbitrary perturbation.

### Author(s)

K.Gerald v.d. Boogaart http://www.stat.boogaart.de, Raimon Tolosana-Delgado

### References

Aitchison, J. (1986) The Statistical Analysis of Compositional Data Monographs on Statistics and Applied Probability. Chapman & Hall Ltd., London (UK). 416p.

Aitchison, J, C. Barcel'o-Vidal, J.J. Egozcue, V. Pawlowsky-Glahn (2002) A consise guide to the algebraic geometric structure of the simplex, the sample space for compositional data analysis, Terra Nostra, Schriften der Alfred Wegener-Stiftung, 03/2003

Billheimer, D., P. Guttorp, W.F. and Fagan (2001) Statistical interpretation of species composition, Journal of the American Statistical Association, 96 (456), 1205-1214

Pawlowsky-Glahn, V. and J.J. Egozcue (2001) Geometric approach to statistical analysis on the simplex. SERRA 15(5), 384-398

http://ima.udg.es/Activitats/CoDaWork03

http://ima.udg.es/Activitats/CoDaWork05

### See Also

`plot.aplus`, `kingTetrahedron` (for 3D-plot), `qqnorm.acomp`,`boxplot.acomp`

### Examples

    data(SimulatedAmounts)
    plot(acomp(sa.lognormals))
    plot(rcomp(sa.lognormals))
    plot(acomp(sa.lognormals5),pca=TRUE)
    plot(rcomp(sa.lognormals5),pca=TRUE)

[Package compositions version 0.91-6 Index]