tclustICplot | R Documentation |
c
and k
, based on the solutions obtained by tclustIC
The 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,
...
)
out |
An S3 object of class |
whichIC |
Specifies the information criterion to use for the plot. See codetclustIC() for the possible values of whichIC. |
tag |
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. |
datatooltip |
Interactive clicking. It is inactive if this parameter is set to FALSE.
If
If datatooltip is a list it may contain the following fields:
|
databrush |
Interactive mouse brushing. If databrush is missing or empty (default), no brushing is done.
The activation of this option (databrush is If If
|
nameY |
Add variable labels in plot. A vector of strings of length |
trace |
Whether to print intermediate results. Default is |
... |
potential further arguments passed to lower level functions. |
FSDA team, valentin.todorov@chello.at
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.
tclustIC
, tclustfsda
## Not run:
data(geyser2)
out <- tclustIC(geyser2, whichIC="MIXMIX", plot=FALSE, alpha=0.1)
tclustICplot(out, whichIC="MIXMIX")
## End(Not run)
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