Description Usage Arguments Details References Examples
Plots clustered curves and/or cluster centers and other results.
1 2 3 4 5 6 7 8 |
x |
An object of class |
y |
Not used. |
data |
Data to include in plot. If the cluster object |
select |
Select the methods, you want to generate the plot for. |
type |
Plot type, see details. |
showLegend |
If |
legendPlace |
Legend placement. |
main |
Plot title, can be missing. |
... |
Further plotting parameters |
If data was clustered by funcit
with
save.data
=TRUE
, different plots can be used. Some plots
are available for all methods, others depend on method which was
used. The plot types are listed below.
If method specific plots are used, method must be extracted by
select
=method name
, see examples.
Plots data and cluster centers.
Plots only cluster centers.
Creates a shadow plot (see function shadow
in package
flexclust - Leisch 2010).
Multiple plots for each cluster. Thickness of lines corresponds to the proximity to the cluster centers. Thicker lines means curve is closer to its center.
Only if baseType
="eigenbasis"
in funcyCtrl
.
Plots the smoothed mean function, covariance matrix and
eigenbasis.
Plots discriminant functions to show the time points of maximum discrimination between clusters (see James2003).
Plots confidence intervals for the curves.
Plots curve locations, temporal trends and overall trends (see Serban2012). For the spatial coefficients, dots are colored according to spatial dependency from yellow to blue. Darker dots mean stronger dependency.
Christina Yassouridis and Dominik Ernst and Friedrich Leisch. Generalization, Combination and Extension of Functional Clustering Algorithms: The R Package funcy. Journal of Statistical Software. 85 (9). 1–25. 2018
Friedrich Leisch. Neighborhood graphs, stripes and shadow plots for cluster visualization. Statistics and Computing. 20(4). 457–469. 2010
Gareth James and Catherine A. Sugar. Clustering for Sparsely Sampled Functional Data. Journal of the American Statistical Association. 98 (462). 297–408. 2003
Nicoleta Serban and Huijing Jiang.Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility. Technometrics. 54 (2). 108–119. 2012
1 2 3 4 5 6 7 8 9 10 11 | set.seed(2804)
ds <- sampleFuncy(obsNr=60, k=4, timeNrMin=5, timeNrMax=10, reg=FALSE)
data <- Data(ds)
clusters <- Cluster(ds)
res <- funcit(data=data, clusters=clusters,
methods=c("fitfclust","distclust", "iterSubspace") ,
k=4, parallel=TRUE)
plot(res)
plot(res, select="fitfclust", type="conf")
plot(res, select="fitfclust", type="discrim")
plot(res, select="distclust", type="shadow")
|
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