Fuzzy c-medoids clustering on tensor-product spline coefficients

cluster.coefs() is used to apply the fuzzy c-medoids clustering algorithm to the tensor-product spline coefficients that are output from the TPSfit() function.

Usage

cluster.coefs(TPSdata, k, m = 1.2, noise = TRUE, seed, ...)

Arguments

TPSdata: Output from the TPSfit() function.
k: An integer value indicating the number of clusters.
m: A numeric value called the 'fuzziness parameter.' Must be greater than 1, with a default of 1.2.
noise: A logical expression indicating whether a noise cluster for outliers is to be included.
seed: An optional numeric value for the seed, as the clustering algorithm uses random start values.
...: Additional optional values employed by fclust, including RS, the number of random starts, and delta, the distance for outliers.

Value

An object of class 'FKM.TPS' containing the following components:

FKM_TPS An object output by the fclust algorithm.
U A dataframe containing the degrees of cluster membership for each subject.
Umax A vector containing the modal cluster assignment for each subject.
FKM_TPS_U A data frame containing subjects with subject ID, degrees of cluster membership, and modal class assignment.
FKM_indices A vector containing the values for six cluster validity indices. See fclust::Fclust.index()
wide_data A data frame with the values that were clustered on in the clustering algorithm plus subject IDs.
TPSdata The 'TPSfit' object that was input into the function.
noise Logical value that was used to determine whether a noise cluster is included.
k The number of clusters.
m The fuzziness parameter.
IDmatch A data frame matching the original subject ID with new consecutive ID values.

Details

cluster.coefs() employs package fclust to apply th fuzzy c-medoids clustering algorithm to the tensor-product spline coefficients that are output from the TPSfit() function. The user has the option to include a noise cluster.

Examples

data(TS.sim)

fitsplines <- TPSfit(TS.sim, vars=c("Var1", "Var2", "Var3"), time="Time",
     ID="SubjectID", knots_time=c(0, 91, 182, 273, 365), n_fit_times=10)

clusters2 <- cluster.coefs(fitsplines, k=3, m=1.3, seed=12345, RS=5, noise=TRUE)
summary(clusters2)
#> cluster.coefs(TPSdata = fitsplines, k = 3, m = 1.3, noise = TRUE, 
#>     seed = 12345, RS = 5)
#> 
#> 150 subjects clustered into 3 clusters + noise cluster using m = 1.3 
#> Clusters based on tensor-product splines coefficients
#> 
#> Cluster summary:
#>       Cl.size Min.degree Max.degree Mean.degree
#> Clus1      30      0.326      1.000       0.487
#> Clus2      49      0.354      1.000       0.921
#> Clus3      43      0.349      1.000       0.675
#> Noise      28      0.311      0.999       0.576
#> 
#> Component 'FKM_TPS' contains the fuzzy clustering details from package 'fclust'.
#> Components of 'FKM_TPS':
#>  [1] "U"         "H"         "F"         "clus"      "medoid"    "value"    
#>  [7] "criterion" "iter"      "k"         "m"         "ent"       "b"        
#> [13] "vp"        "delta"     "stand"     "Xca"       "X"         "D"        
#> [19] "call"     
#> 
#> Cluster validity indices:
#>        PC        PE       MPC       SIL     SIL.F        XB 
#> 0.5338480 0.5881223 0.3007721 0.1190551 0.1316716 2.3993223 
#> 
#> Output dataset 'FKM_TPS_U' contains degree of cluster membership and modal
#>       cluster assignment for each object.
#> Head of dataset 'FKM_TPS_U':
#>   Id2 SubjectID        Clus1     Clus2        Clus3        Noise ClusModal
#> 1   1        17 0.0001836595 0.9994971 0.0003074245 1.177236e-05         2
#> 2   2        19 0.4575291735 0.1261526 0.2864494603 1.298688e-01         1
#> 3   3        34 0.0020179751 0.9953520 0.0025581026 7.187532e-05         2
#> 4   4        52 0.0016033172 0.9964037 0.0018971158 9.585506e-05         2
#> 5   5        55 0.1687127692 0.2410988 0.2030380462 3.871503e-01         0
#> 6   6        83 0.1446869270 0.2082775 0.1342187362 5.128169e-01         0
#>