kclustering {BasketballAnalyzeR} | R Documentation |
K-means cluster analysis
kclustering(
data,
k = NULL,
labels = NULL,
nclumax = 10,
nruns = 10,
iter.max = 50,
algorithm = "Hartigan-Wong"
)
data |
numeric data frame. |
k |
integer, number of clusters. |
labels |
character, row labels. |
nclumax |
integer, maximum number of clusters (when |
nruns |
integer, run the k-means algorithm |
iter.max |
integer, maximum number of iterations allowed in k-means clustering (see kmeans). |
algorithm |
character, the algorithm used in k-means clustering (see kmeans). |
The kclustering
function performs a preliminary standardization of columns in data
.
A kclustering
object.
If k
is NULL
, the kclustering
object is a list of 3 elements:
k
NULL
clusterRange
integer vector, values of k
(from 1 to nclumax
) at which the variance between of the clusterization is evaluated
VarianceBetween
numeric vector, values of the variance between evaluated for k
in clusterRange
If k
is not NULL
, the kclustering
object is a list of 4 elements:
k
integer, number of clusters
Subjects
data frame, subjects' cluster identifiers
ClusterList
list, clusters' composition
Profiles
data frame, clusters' profiles, i.e. the average of the variables within clusters and the cluster eterogeineity index (CHI
)
Marco Sandri, Paola Zuccolotto, Marica Manisera (basketballanalyzer.help@unibs.it)
P. Zuccolotto and M. Manisera (2020) Basketball Data Science: With Applications in R. CRC Press.
FF <- fourfactors(Tbox,Obox)
X <- with(FF, data.frame(OD.Rtg=ORtg/DRtg,
F1.r=F1.Def/F1.Off, F2.r=F2.Off/F2.Def,
F3.O=F3.Def, F3.D=F3.Off))
X$P3M <- Tbox$P3M
X$STL.r <- Tbox$STL/Obox$STL
kclu1 <- kclustering(X)
plot(kclu1)
kclu2 <- kclustering(X, k=9)
plot(kclu2)