modelSelection_Q {ppsbm}

Selects the number of groups with ICL

Description

Selects the number of groups with Integrated Classification Likelihood Criterion

Usage

modelSelection_Q(data, n, Qmin = 1, Qmax, directed = TRUE, sparse = FALSE,
  sol.hist.sauv)

Arguments

data

List with 2 components:

• $Time - [0,data$Time] is the total time interval of observation

• $Nijk - data matrix with the statistics per process N_{ij} and sub-intervals k

n

Total number of nodes n

Qmin

Minimum number of groups

Qmax

Maximum number of groups

directed

Boolean for directed (TRUE) or undirected (FALSE) case

sparse

Boolean for sparse (TRUE) or not sparse (FALSE) case

sol.hist.sauv

List of size Qmax-Qmin+1 obtained from running mainVEM(data,n,Qmin,Qmax,method='hist')

References

BIERNACKI, C., CELEUX, G. & GOVAERT, G. (2000). Assessing a mixture model for clustering with the integrated completed likelihood. IEEE Trans. Pattern Anal. Machine Intel. 22, 719–725.

CORNELI, M., LATOUCHE, P. & ROSSI, F. (2016). Exact ICL maximization in a non-stationary temporal extension of the stochastic block model for dynamic networks. Neurocomputing 192, 81 – 91.

DAUDIN, J.-J., PICARD, F. & ROBIN, S. (2008). A mixture model for random graphs. Statist. Comput. 18, 173–183.

MATIAS, C., REBAFKA, T. & VILLERS, F. (2018). A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika.

Examples

# load data of a synthetic graph with 50 individuals and 3 clusters
n <- 50

# compute data matrix with precision d_max=3
Dmax <- 2^3
data <- list(Nijk=statistics(generated_Q3$data,n,Dmax,directed=FALSE),
    Time=generated_Q3$data$Time)

# ICL-model selection
sol.selec_Q <- modelSelection_Q(data,n,Qmin=1,Qmax=4,directed=FALSE,
    sparse=FALSE,generated_sol_hist)

# best number Q of clusters:
sol.selec_Q$Qbest