Description Usage Arguments Value References See Also Examples
Latent class analysis (LCA) can be used to find groups in the sender nodes (with the condition of independence within the groups). For more details see Gollini, I. (in press) and Gollini, I., and Murphy, T. B. (2014).
1 | lca(X, G, nstarts = 3, tol = 0.1^2, maxiter = 250)
|
X |
( |
G |
number of groups |
nstarts |
integer number of different starts for the EM algorithm. Default |
tol |
desired tolerance for convergence. Default |
maxiter |
maximum number of iterations. Default |
List containing the following information for each model fitted:
p
(G
x M
) matrix containing the conditional probability of observing a link to sender nodes if the receiver nodes are from group g.
eta
η_g is the mixing proportion for the group g (g = 1,..., G), that corresponds to the prior probability that a randomly chosen sender node is in the g-th group.
z
(N
x G
) matrix containing posterior probability for each sender node to belong to each group
LL
log likelihood
BIC
Bayesian Information Criterion (BIC) (Schwarz (1978))
If multiple models are fitted the output contains also a table to compare the BIC for all models fitted.
Gollini, I. (in press) 'A mixture model approach for clustering bipartite networks', Challenges in Social Network Research Volume in the Lecture Notes in Social Networks (LNSN - Series of Springer). Preprint: https://arxiv.org/abs/1905.02659.
Gollini, I., and Murphy, T. B. (2014), 'Mixture of Latent Trait Analyzers for Model-Based Clustering of Categorical Data', Statistics and Computing, 24(4), 569-588 http://arxiv.org/abs/1301.2167.
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