plot.acomp {compositions} | R Documentation |
## 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"))
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. In case of 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 |
A 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
philosophy. See scale |
scale |
a logical indicating whether a the data should be
scaled prior to the plot. Scaling is done in the choosen
philosophy. See scale |
pca |
A logical indicating whether the first principle component should be displayed in the plot. Currently direction of the principle component of the displayed subcomposition is displayed as a line. Later on a the principle componenent of the whole dataset should be displayed. |
col.pca |
The color to draw the principle component. |
The data is displayed in ternary diagrams. This 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 arrange in a scatterplot matrix through the
command pairs
.
The third component in each
of the panels is than choosen 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 a composition only containing the single part
displayed in that corner. Points on the edges correspond to
compositions only containing the parts in the adjacent corners. The
relative amounts are displayed by the distance to the opposite
corner. The individual portions of general points can be infered by
imaginatorily drawing a line parallel to the edge opposite to the
corner of the part of interest through the point. The portion of the
part of intrest is constant along the line. Thus we can read it
on both crossing points of the line with the edges.
Relative portions of two parts can be inferred by imaginatorily
drawing a line through the point and the corner of the unimportant
component. This line intersects the edge between the two components
of interest in the composition with the same relative portion of the
two remaining components.
Exactly the lines parallel to one of the edges or going through one of
the corners are straight lines as well in Aitchison and as in real
geometry. They remain straight under an arbitrary perturbation.
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
plot.aplus
,
qqnorm.acomp
,boxplot.acomp
data(SimulatedAmounts) plot(acomp(sa.lognormals)) plot(rcomp(sa.lognormals)) plot(acomp(sa.lognormals5),pca=TRUE) plot(rcomp(sa.lognormals5),pca=TRUE)