Description Usage Arguments Details Value Author(s) References Examples
Model-based clustering and cluster-specific parameter estimation through the mixed membership Exponential-Family Random Graph Models (ERGMs) for the different number of clusters. Model selection is based on maximum value of Integrated Classification Likelihood (ICL).
1 2 | ergmclust.ICL(adjmat, Kmax = 5, directed = FALSE,
thresh = 1e-06, iter.max = 200, coef.init = NULL)
|
adjmat |
An object of class matrix of dimension (N x N) containing the adjacency matrix, where N is the number of nodes in the network. |
Kmax |
Maximum number of clusters. |
directed |
If |
thresh |
Optional user-supplied convergence threshold for relative error in the objective in Variational Expectation-Maximization (VEM) algorithm. The default value is set as 1e-06. |
iter.max |
The maximum number of iterations after which the algorithm is terminated. The default value is set as 200. |
coef.init |
Optional user-supplied network canonical parameter vector (K-dimensional). Default is |
ergmclust.ICL is an R implementation for the model selection for an appropriate number of clusters in the mixed membership Exponential-Family Random Graph Models (ERGMs). The Integrated Classification Likelihood (ICL) was proposed by Biernacki et al. (2000) and Daudin, et. al. (2008) to assess the model-based clustering.
Returns a list of ergmclust
object. Each object of class ergmclust
is a list
with the following components:
Kselect |
Optimum number of clusters chosen after model selection through Integrated Classification Likelihood (ICL). |
coefficients |
An object of class vector of size (Kselect x 1) containing the canonical network parameters of the model. |
probability |
An object of class matrix of size (N x Kselect) containing the mixed membership probabilities of the model for N nodes distributed in Kselect clusters. |
clust.labels |
An object of class vector of size (N x 1) containing the cluster membership labels in {1, ..., Kselect} for N nodes. |
ICL |
Integrated Classification Likelihood (ICL) score calculated from completed data log-likelihood and penalty terms. |
Authors: Amal Agarwal [aut, cre], Kevin Lee [aut], Lingzhou Xue [aut, cre], Anna Yinqi Zhang [cre]
Maintainer: Lingzhou Xue <lzxue@psu.edu>
Biernacki, C., Celeux, G., and Govaert, G. (2000) Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22(7), 719-725
https://ieeexplore.ieee.org/document/865189
Daudin, J. J., Picard, F., and Robin, S. (2008) A Mixture Model for Random Graphs, Statistics and Computing, Vol. 18(2), 173–183
https://link.springer.com/article/10.1007/s11222-007-9046-7
1 2 3 4 5 6 7 8 9 10 11 | ## undirected network:
data(tradenet)
## Model selection for Kmax = 3
ergmclust.ICL(adjmat = tradenet, Kmax = 3, directed = FALSE,
thresh = 1e-06, iter.max = 120, coef.init = NULL)
## directed network:
data(armsnet)
## Model selection for Kmax = 3
ergmclust.ICL(adjmat = armsnet, Kmax = 3, directed = TRUE,
thresh = 1e-06, iter.max = 60, coef.init = NULL)
|
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