This function displays multivariate unclosed amout datasets classes "aplus" and "rplus" in a way respecting the choosen geometry eventually in log scale.
# S3 method for aplus
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"),
add=FALSE,logscale=TRUE,xlim=NULL,ylim=xlim,
col=par("col"),plotMissings=TRUE,
lenMissingTck=0.05,colMissingTck="red",
mp=~simpleMissingSubplot(missingPlotRect,missingInfo,
c("NM","TM",cn)),
robust=getOption("robust"))
# S3 method for rplus
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"),
add=FALSE,logscale=FALSE,
xlim=NULL,
ylim=xlim,col=par("col"),plotMissings=TRUE,
lenMissingTck=0.05,colMissingTck="red",
mp=~simpleMissingSubplot(missingPlotRect,missingInfo,
c("NM","TM",cn)),
robust=getOption("robust"))
# S3 method for rmult
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"),
add=FALSE,logscale=FALSE,col=par("col"),
robust=getOption("robust"))
a dataset with class aplus, rplus or rmult
further graphical parameters passed (see
par
)
a logical indicating whether the information should just be added to an existing plot. If FALSE, a new plot is created
the color to plot the data
logical indicating that missingness should be
represented graphically. Componentes with one missing subcomponent
in the plot are represented by tickmarks at the two
axis. Cases with two missing components are only
represented in a special panel drawn according to the mp
parameter
if missings are present. Missings of type BDL (below detection
limit) are always plotted in nonlogaritmic plots, even if
plotMissings
is false, but
in this case this fact is not specially marked.
length of the tick-marks (in portion of the plotting region) to be plotted for missing values. If 0 no tickmarks are plotted. Negative lengths point outside of the plot. A length of 1 runs right through the whole plot.
colors to draw the missing tick-marks. NULL means to take the colors specified for the observations.
A formula providing a call to a function plotting
informations on the missings. The call is evaluted in the
environment of the panel plotting function and has access (among
others) to: cn
the names of the components in the current
plot, x
the dataset of the current plot, missingInfo
is a table giving the number of
observations of the types NM=Non Missing, TM=Totally missing
(i.e. two components of the subcomposition are missing),
and the two single component missing possibilities.
the labels for names of the parts
the names of the parts to be used in a single panel. Internal use only
logical indicating that only a single panel should be drawn and not the whole plot. Internal use only
a logical. If TRUE one can identify the points like with the
identify
command
A character vector providing the labels to be used with
the identification, when id=TRUE
color of the idlabs
labels
a logical indicating whether the data should be
centered prior to the plot. Centering is done in the chosen
geometry. See scale
a logical indicating whether the data should be
scaled prior to the plot. Scaling is done in the chosen
geometry. See scale
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.
the color to draw the principal component.
logical indicating whether a log scale should be used
2xncol(x)-matrix giving the xlims for the columns of x
2xncol(x)-matrix giving the ylims for the columns of x
A robustness description. See robustnessInCompositions for details. The option is used for centering, scaling and principle components.
TO DO: fix pca bug
# NOT RUN {
data(SimulatedAmounts)
plot(aplus(sa.lognormals))
plot(rplus(sa.lognormals))
plot(aplus(sa.lognormals5))
plot(rplus(sa.lognormals5))
# }
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