This class contains the parameters in the output after running classification.
a matrix of size (number of Curves) x (K), each column contains the weights of the curves in the corresponding class.
a vector of size number of curves, containing the index of the class for each curve.
a matrix of size 1xnbClust (number of clusters), containing the estimated mixture proportions.
the estimated log-likelihood.
the value of AIC criterion.
the value of BIC criterion.
the value of ICL criterion.
a vector of size nbClust of the dimensions of the specifique dimensions of the functional data in each class.
a matrix of size nbClust x nbRunIteration, where nbRunIteration is the number of iterations before the algorithm converge. Each column of the dimTotal matrix contain the dimensions on the coresponding iteration.
principal components variances per cluster
logical parameter, and empty=TRUE if we have an empty class