Description Usage Arguments Details Value Author(s) References Examples
Main function for clustering functional data according to one or several of seven algorithms.
1 2 3 4 |
data |
Data in format "Format1" or format "Format2" (see |
k |
Number of clusters. |
methods |
For a detailed description of the methods please see the references. |
seed |
Seed for initial clustering. See |
regTime |
If data is in "Format2", optional vector representing the
time points (see |
clusters |
Optional vector of true cluster labels. |
funcyCtrl |
A control object of class |
fpcCtrl |
A control object of class |
parallel |
If |
save.data |
Save a copy of the |
... |
Additional optional model specific parameters. Works only if exactly one method
is called in
|
funcit
is the core function to execute one or more methods to cluster functional
data. Functional data can be measured on a regular or on an irregular
grid. While for regular datasets, all curves are measured on the same
time points, for irregular datasets, number or/and location of time
points can differ (see formatFuncy
for different formats). Only algorithms "fitfclust"
,"distclust"
and
"iterSubspace"
are applicable to irregular datasets.
All methods are based on the projection of the curves onto a
basis defined in funcyCtrl
and building mixed effects
models of the basis coefficients.
Returns an object of class funcyOutList
.
Christina Yassouridis
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
Gareth James and Catherine A. Sugar. Clustering for Sparsely Sampled Functional Data. Journal of the American Statistical Association. 98 (462). 297–408. 2003
Jie Peng and Hans-Georg Mueller. Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions. The Annals of Applied Statistics. 2 (3). 1056–1077, 2008
Chiou Jeng-Min and Pai-Ling Li. Functional clustering and identifying substructures of longitudinal data. Journal of the Royal Statistical Society: Series B. 69 (4). 679–699. 2007
Madison Giacofci and Sophie Lambert-Lacroix and Guillemette Marot and Franck Picard. Wavelet-based clustering for mixed-effects functional models in high dimension. Biometrics. 69. 31–40. 2011
Nicoleta Serban and Huijing Jiang.Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility. Technometrics. 54 (2). 108–119. 2012
Julien Jacques and Cristian Preda. Funclust: a curves clustering method using functional random variables density approximation. Neurocomputing. 112. 164–171. 2013
Charles Bouveyron and Julien and Jacques. Model-based clustering of time series in group-specific functional subspaces. Advances in Data Analysis and Classification. 5 (4). 281–300. 2011
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | ##Cluster the data with methods for regular sets
##Sample a regular dataset
set.seed(2804)
ds <- sampleFuncy(obsNr=50, k=4, timeNr=8, reg=TRUE)
##Cluster the functions with all available methods.
res <- funcit(data=Data(ds), clusters=Cluster(ds),
methods=c(1,2,3), seed=2404,
k=4)
summary(res)
Cluster(res)
##Additional method specific parameters for method fitfclust
res <- funcit(data=Data(ds), clusters=Cluster(ds), methods="fitfclust", seed=2405,
k=4, p=5, pert=0)
##Cluster the data with methods for irregular sets
##Sample an irregular dataset
set.seed(2804)
ds <- sampleFuncy(obsNr=50, k=4, timeNrMin=4, timeNrMax=8,
reg=FALSE)
data <- Data(ds)
clusters <- Cluster(ds)
res <- funcit(data=data, clusters=clusters,
methods=c("fitfclust","distclust", "iterSubspace"), seed=2406,
k=4, parallel=TRUE)
summary(res)
Cluster(res)
plot(res)
##Two reallife examples
## Not run:
data("genes")
data <- genes$data
clusters <- genes$clusters
##Cluster the functions with all available methods.
res <- funcit(data=data, clusters=clusters,
methods=c(1:7)[-4], seed=2404,
k=4)
summary(res)
Cluster(res)
## End(Not run)
## Not run:
data("electricity")
res <- funcit(data=electricity, methods=c("fitfclust","distclust",
"waveclust"), seed=2406, k=5, parallel=TRUE)
plot(res, legendPlace="topleft")
## End(Not run)
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