Functional clustering and identifying substructures of longitudinal data using kCFC.
Functional clustering and identifying substructures of longitudinal data using kCFC.
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")
)y |
A list of n vectors containing the observed values for each individual. Missing values specified by |
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 |
optnsCS |
A list of options control parameters specified by |
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. |
Jeng-Min Chiou, Pai-Ling Li, "Functional clustering and identifying substructures of longitudinal data." Journal of the Royal Statistical Society 69 (2007): 679-699
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.