The funCC algorithm allows to simultaneously cluster the rows and the columns of a data matrix where each entry of the matrix is a function or a time series
funcc_biclust(
fun_mat,
delta,
theta = 1,
template.type = "mean",
number = 100,
alpha = 0,
beta = 0,
const_alpha = FALSE,
const_beta = FALSE,
shift.alignement = FALSE,
shift.max = 0.1,
max.iter.align = 100
)The data array (n x m x T) where each entry corresponds to the measure of one observation i, i=1,...,n, for a functional variable m, m=1,...,p, at point t, t=1,...,T
scalar: Maximum of accepted score, should be a real value > 0
scalar: Scaling factor should be a real value > 1
character: type of template required. If template.type='mean' the template is evaluated as the average function, if template.type='medoid' the template is evaluated as the medoid function.
integer: Maximum number of iteration
binary: if alpha=1 row shift is allowed, if alpha=0 row shift is avoided
binary: if beta=1 row shift is allowed, if beta=0 row shift is avoided
logicol: Indicates if row shift is contrained as constant.
logicol: Indicates if col shift is contrained as constant.
logicol: If shift.alignement=True the shift aligment is performed, if shift.alignement=False no alignment is performed
scalar: shift.max controls the maximal allowed shift, at each iteration, in the alignment procedure with respect to the range of curve domains. t.max must be such that 0<shift.max<1
integer: maximum number of iteration in the alignment procedure
a list of two elements containing respectively the Biclustresults and a dataframe containing the parameters setting of the algorithm @examples data("funCCdata") res <- funcc_biclust(funCCdata,delta=10,theta=1,alpha=1,beta=0,const_alpha=TRUE) res