Description Usage Arguments Value Author(s) Examples
This function runs a co-clustering algorithm on ordinal data by using the latent block model (see references for further details). A BOS distribution is used, and the parameters inference is obtained using the SEM-Gbbs algorithm.
1 2 | boscoclust(x=matrix(0,nrow=1,ncol=1), idx_list=c(1), kr, kc, init, nbSEM, nbSEMburn,
nbRepeat=1, nbindmini, m=0, percentRandomB=0)
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x |
Matrix made of ordinal data of dimension N*Jtot. The features with the same numbers of levels must be placed side by side. The missing values should be coded as NA. |
idx_list |
Vector of length D. This argument is useful when variables have different numbers of levels. Element d should indicate where the variables with number of levels m[d] begin in matrix x. |
kr |
Number of row classes. |
kc |
Vector of length D. The d^th element indicates the number of column clusters. |
m |
Vector of length D. The d^th element defines the number of levels of the ordinal data. |
nbSEM |
Number of SEM-Gibbs iterations realized to estimate parameters. |
nbSEMburn |
Number of SEM-Gibbs burn-in iterations for estimating parameters. This parameter must be inferior to nbSEM. |
nbRepeat |
Number of times sampling on rows and columns will be done for 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 the following words : "kmeans", "random" or "randomBurnin". |
percentRandomB |
Vector of length 2. Indicates the percentage of resampling when init is equal to "randomBurnin". |
@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 |
Name of the result. |
@paramschain |
List of length nbSEMburn. The parameters of the blocks are stored for each iteration of the SEM-Gibbs algorithm. |
@pichain |
List of length nbSEM. Item i is a vector of length kr that contains the row mixing proportions at iteration i. |
@rhochain |
List of length nbSEM. Item i is a list of length D whose d^th element contains the column mixing proportions of the group of variables d, for iteration i. |
@zc |
List of length D. The d^th item is a vector of length J[d] representing the column 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. The d^th element represents the number of levels of d^th group of variables. |
@params |
List of length D. The d^th item represents the blocks parameters for a group of variables d. |
@pi |
Vector of length kr. This corresponds to the row mixing proportions. |
@rho |
List of length D. The d^th item represents the column mixing proportion for the d^th group of variables. |
@xhat |
List of length D. The d^th item represents the dataset of the d^th group of variables, 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 |
library(ordinalClust)
# loading the real dataset
data("dataqol")
set.seed(5)
# loading the ordinal data
M <- as.matrix(dataqol[,2:29])
# defining different number of categories:
m=4
# defining number of row and column clusters
krow = 4
kcol = 4
# configuration for the inference
nbSEM=50
nbSEMburn=40
nbindmini=2
init = "randomBurnin"
percentRandomB=c(20,20)
# Co-clustering execution
object <- boscoclust(x=M,kr=krow,kc=kcol,m=m,nbSEM=nbSEM,
nbSEMburn=nbSEMburn, nbindmini=nbindmini, init=init, percentRandomB=percentRandomB)
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Attaching package: ‘ordinalClust’
The following object is masked from ‘package:graphics’:
plot
The following objects are masked from ‘package:base’:
plot, summary
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