kmm {MixAll} | R Documentation |
KmmModel
] classThis function computes the optimal kernel mixture model (KMM) according
to the [criterion
] among the number of clusters given in
[nbCluster
], using the strategy specified in [strategy
].
kmm(data, nbCluster = 2, dim = 10, models = "kmm_pk_s", kernelName = "Gaussian", kernelParameters = c(1), kernelComputation = TRUE, strategy = kmmStrategy(), criterion = "ICL", nbCore = 1)
data |
frame or matrix containing the data. Rows correspond to observations and columns correspond to variables. |
nbCluster |
[ |
dim |
integer giving the dimension of the Gaussian density. Default is 10. |
models |
[ |
kernelName |
string with a kernel name. Possible values: "Gaussian", "polynomial", "Laplace", "linear", "rationalQuadratic_", "Hamming". Default is "Gaussian". |
kernelParameters |
[ |
kernelComputation |
[ |
strategy |
a [ |
criterion |
character defining the criterion to select the best model. The best model is the one with the lowest criterion value. Possible values: "BIC", "AIC", "ICL", "ML". Default is "ICL". |
nbCore |
integer defining the number of processor to use (default is 1, 0 for all). |
An instance of the [KmmModel
] class.
in KmmModel instance returned, the gram matrix is computed if and only
if kernelComputation is TRUE
.
Serge Iovleff
## A quantitative example with the famous bulls eye model data(bullsEye) ## estimate model model <- kmm( data=bullsEye, nbCluster=2:3, models= "kmm_pk_s") ## get summary summary(model) ## use graphics functions ## Not run: plot(model) ## End(Not run)