Description Usage Arguments References Examples
Selects the number of groups with Integrated Classification Likelihood Criterion
1 2  modelSelection_Q(data, n, Qmin = 1, Qmax, directed = TRUE, sparse = FALSE,
sol.hist.sauv)

data 
List with 2 components:

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 QmaxQmin+1 obtained from running mainVEM(data,n,Qmin,Qmax,method='hist') 
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 nonstationary 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # 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$Time)
# ICLmodel selection
sol.selec_Q < modelSelection_Q(data,n,Qmin=1,Qmax=4,directed=FALSE,
sparse=FALSE,generated_sol_hist)
# best number Q of clusters:
sol.selec_Q$Qbest

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