Description Usage Arguments Value Author(s) Examples
This function performs a co-clustering on heterogeneous data sets by using the Multiple Latent Block model (cf references for further details).
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x |
Data matrix, of dimension N*Jtot. The features with same type should be aside. The missing values should be coded as NA. |
idx_list |
Vector of length D. This argument is useful when variables are of different types. Element d should indicate where the variables of type d begins in matrix x. |
distrib_names |
Vector of length D. indicates the type of distribution to use. Must be among "Gaussian", "Multinomial", "BOS", "Poisson" or "Functional". Functional data must always be at the end. |
kr |
Number of row classes. |
kc |
Vector of length D. d^th element indicates the number of column clusters. |
m |
Vector of length D. d^th element defines the ordinal and categorical data's number of levels. |
functionalData |
Data tensor of dimension N*J*T. |
nbSEM |
Number of SEM-Gibbs iterations realized to estimate parameters. |
nbSEMburn |
Number of SEM-Gibbs burning iterations for estimating parameters. This parameter must be inferior to nbSEM. |
nbRepeat |
Number of times sampling on rows and on colums will be done at each SEM-Gibbs iteration. |
nbindmini |
Minimum number of cells belonging to a block. |
init |
String that indicates the kind of initialisation. Must be one of th following words : "kmeans", "random", "provided", "randomParams" or "randomBurnin". |
zrinit |
Vector of length N. When init="provided", indicates the labels of each row. |
zcinit |
Vector of length Jtot. When init="provided", indicates the labels of each column. |
percentRandomB |
Vector of length 2. Indicates the percentage of resampling when init is equal to "randomBurnin". |
percentRandomP |
Vector of length 2. Indicates the percentage of resampling when init is equal to "randomParams". |
@V |
Matrix of dimension N*kr such that V[i,g]=1 if i belongs to cluster g. |
@icl |
ICL value for co-clustering. |
@name |
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@paramschain |
List of length nbSEMburn. For each iteration of the SEM-Gibbs algorithm, the parameters of the blocks are stored. |
@pichain |
List of length nbSEM. Item i is a vector of length kr which contains the row mixing proportions at iteration i. |
@rhochain |
List of length nbSEM. Item i is a list of length D whose d^th contains the column mixing proportions of groups of variables d, at iteration i. |
@zc |
List of length D. d^th item is a vector of length J[d] representing the columns partitions for the group of variables d. |
@zr |
Vector of length N with resulting row partitions. |
@W |
List of length D. Item d is a matrix of dimension J*kc[d] such that W[j,h]=1 if j belongs to cluster h. |
@m |
Vector of length D. d^th element represents the number of levels of d^th group of variables. |
@params |
List of length D. d^th item represents the blocks paramaters for group of variables d. |
@pi |
Vector of length kr. Row mixing proportions. |
@rho |
List of length D. d^th item represents the column mixing proportion for d^th group of variables. |
@xhat |
List of length D. d^th item represents the d^th group of variables dataset, with missing values completed. |
@zrchain |
Matrix of dimension nbSEM*N. Row i represents the row cluster partitions at iteration i. |
@zrchain |
List of length D. Item d is a matrix of dimension nbSEM*J[d]. Row i represents the column cluster partitions at iteration i. |
Margot Selosse, Julien Jacques, Christophe Biernacki.
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data(M1)
nbSEM=30
nbSEMburn=20
nbindmini=1
init = "random"
kr=2
kc=c(2,2,2)
m=c(6,3)
d.list <- c(1,41,81)
distributions <- c("Multinomial","Gaussian","Bos")
res <- mixedCoclust(x = M1, idx_list = d.list,distrib_names = distributions,
kr = kr, kc = kc, m = m, init = init,nbSEM = nbSEM,
nbSEMburn = nbSEMburn, nbindmini = nbindmini)
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