kCFC: Functional clustering and identifying substructures of...

In fdapace: Functional Data Analysis and Empirical Dynamics

Functional clustering and identifying substructures of longitudinal data using kCFC.

Description

Functional clustering and identifying substructures of longitudinal data using kCFC.

Usage

kCFC(
  y,
  t,
  k = 3,
  kSeed = 123,
  maxIter = 125,
  optnsSW = list(methodMuCovEst = "smooth", FVEthreshold = 0.9, methodBwCov = "GCV",
    methodBwMu = "GCV"),
  optnsCS = list(methodMuCovEst = "smooth", FVEthreshold = 0.7, methodBwCov = "GCV",
    methodBwMu = "GCV")
)

Arguments

y	A list of n vectors containing the observed values for each individual. Missing values specified by NAs are supported for dense case (dataType='dense').
t	A list of n vectors containing the observation time points for each individual corresponding to y.
k	A scalar defining the number of clusters to define; default 3. Values that define very small clusters (eg.cluster size <=3) will potentially err.
kSeed	A scalar valid seed number to ensure replication; default: 123
maxIter	A scalar defining the maximum number of iterations allowed; default 20, common for both the simple kmeans initially and the subsequent k-centres
optnsSW	A list of options control parameters specified by list(name=value) to be used for sample-wide FPCA; by default: "list( methodMuCovEst ='smooth', FVEthreshold= 0.90, methodBwCov = 'GCV', methodBwMu = 'GCV' )". See ‘Details in ?FPCA’.
optnsCS	A list of options control parameters specified by list(name=value) to be used for cluster-specific FPCA; by default: "list( methodMuCovEst ='smooth', FVEthreshold= 0.70, methodBwCov = 'GCV', methodBwMu = 'GCV' )". See ‘Details in ?FPCA’.

Value

A list containing the following fields:

cluster	A vector of levels 1:k, indicating the cluster to which each curve is allocated.
fpcaList	A list with the fpcaObj for each separate cluster.
iterToConv	A number indicating how many iterations where required until convergence.

References

Jeng-Min Chiou, Pai-Ling Li, "Functional clustering and identifying substructures of longitudinal data." Journal of the Royal Statistical Society 69 (2007): 679-699

Examples

data(medfly25) 
Flies <- MakeFPCAInputs(medfly25$ID, medfly25$Days, medfly25$nEggs)
kcfcObj <- kCFC(Flies$Ly[1:150], Flies$Lt[1:150], # using only 150 for speed consideration 
                 optnsSW = list(methodMuCovEst = 'smooth', userBwCov = 2, FVEthreshold = 0.90),
                 optnsCS = list(methodMuCovEst = 'smooth', userBwCov = 2, FVEthreshold = 0.70))

fdapace documentation built on July 3, 2024, 5:08 p.m.