c and k, based on the solutions obtained by tclustICThe function tclustICplot() takes as input an object of class
tclustic.object, the output
of function tclustIC (that is a series of matrices which contain
the values of the information criteria BIC/ICL/CLA for different values of k
and c) and plots them as function of c or of k. The plot enables
interaction in the sense that, if option databrush has been activated, it is
possible to click on a point in the plot and to see the associated classification
in the scatter plot matrix.
tclustICplot(out, whichIC = c("ALL", "MIXMIX", "MIXCLA", "CLACLA"), tag,
datatooltip, databrush, nameY, trace = FALSE, ...)An S3 object of class tclustic.object
(output of tclustIC) containing the values
of the information criteria BIC (MIXMIX), ICL (MIXCLA) or CLA (CLACLA),
for different values of k (number of groups) and different
values of c (restriction factor), for a prespecified level of trimming.
Specifies the information criterion to use for the plot. See codetclustIC() for the possible values of whichIC.
plot handle. String which identifies the handle of the plot which is about to be created. The default is to use tag 'pl_IC'. Notice that if the program finds a plot which has a tag equal to the one specified by the user, then the output of the new plot overwrites the existing one in the same window else a new window is created.
Interactive clicking. It is inactive if this parameter is set to FALSE.
The default is datatooltip=TRUE, the user can select with the mouse a solution
in order to have the following information:
1) value of k which has been selected
2) value of c which has been selected
3) values of the information criterion
4) frequency distribution of the associated classification.
If datatooltip is a list it may contain the following fields:
DisplayStyle determines how the data cursor displays. Possible values are
'datatip' and 'window' (default). 'datatip' displays
data cursor information in a small yellow text box attached to a black
square marker at a data point you interactively select. 'window'
displays data cursor information for the data point you interactively
select in a floating window within the figure.
SnapToDataVertex: specifies whether the data cursor snaps to the nearest data value or
is located at the actual pointer position.
Possible values are SnapToDataVertex='on' (default) and SnapToDataVertex='off'.
Interactive mouse brushing. If databrush is missing or empty (default), no brushing is done.
The activation of this option (databrush is TRUE or a list) enables the user to select
a set of values of IC in the current plot and to see thecorresponding classification highlighted in the scatterplot matrix.
If the scatterplot matrix does not exist it is automatically created.
Note that the window style of the other figures is set equal to that which contains the IC plot.
In other words, if the IC plot is docked all the other figures will be docked too.
If databrush=TRUE the default selection tool is a rectangular brush and it is
possible to brush only once (that is persist='').
If databrush=list(...), it is possible to use all optional arguments of
the MATLAB function selectdataFS() and the following optional arguments:
persist: Persist is an empty value or a character containing 'on' or 'off'.
The default value is persist="", that is brushing is allowed only once.
If persist="on" or persis="off" brushing can be done as many time as
the user requires. If persist='on' then the unit(s) currently brushed are
added to those previously brushed. It is possible, every time a new brushing is
done, to use a different color for the brushed units. If persist='off'
every time a new brush is performed units previously brushed are removed.
label: add labels of brushed units in the monitoring plot.
dispopt: controls how to fill the diagonals in the scatterplot matrix
of the brushed solutions. Set dispopt="hist" (default) to plot histograms,
or dispopt="box" to plot boxplots.
Add variable labels in plot. A vector of strings of length p
containing the labels of the variables in the dataset.
If it is empty (default) the sequence X1, ..., Xp will be created automatically
Whether to print intermediate results. Default is trace=FALSE.
potential further arguments passed to lower level functions.
Cerioli, A., Garcia-Escudero, L.A., Mayo-Iscar, A. and Riani M. (2017). Finding the Number of Groups in Model-Based Clustering via Constrained Likelihoods, emphJournal of Computational and Graphical Statistics, pp. 404-416, https://doi.org/10.1080/10618600.2017.1390469.
Hubert L. and Arabie P. (1985), Comparing Partitions, Journal of Classification, Vol. 2, pp. 193-218.
# NOT RUN {
# }
# NOT RUN {
data(geyser2)
out <- tclustIC(geyser2, whichIC="MIXMIX", plot=FALSE, alpha=0.1)
tclustICplot(out)
# }
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