Selects the number of groups with ICL
Selects the number of groups with Integrated Classification Likelihood Criterion
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 Qmax-Qmin+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 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.
# 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$Time)
# ICL-model selection
sol.selec_Q <- modelSelection_Q(data,n,Qmin=1,Qmax=4,directed=FALSE,
sparse=FALSE,generated_sol_hist)
# best number Q of clusters:
sol.selec_Q$QbestPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.