modelSelection_Q {ppsbm}R Documentation

Selects the number of groups with ICL criterion

Description

Selects the number of groups with Integrated Classification Likelihood (ICL) criterion.

Usage

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

Arguments

data

List with 2 components:

  • Time - Positive real number. [0,Time] is the total time interval of observation.

  • Nijk - Data matrix with the statistics per process N_{ij} and sub-intervals 1\le k\le K.

n

Total number of nodes, 1\le i \le 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 on the data with method='hist'.

Value

The function outputs a list of 7 components:

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. 105(3): 665-680.

Examples

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

# compute data matrix of counts per subinterval with precision d_max=3
# (ie nb of parts K=2^{d_max}=8).
K <- 2^3
data <- list(Nijk=statistics(generated_Q3$data,n,K,directed=FALSE),
    Time=generated_Q3$data$Time)

# ICL-model selection with groups ranging from 1 to 4
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


[Package ppsbm version 1.0.0 Index]