generated_sol_hist {ppsbm} | R Documentation |
Output example of mainVEM
Description
Output of mainVEM obtained on dataset generated_Q3
with hist
method and Qmin=1, Qmax=5.
Usage
generated_sol_hist
Format
List of 5 components.
Each one is the output of the algorithm with a different value of the number of clusters Q
for 1\le Q \le 5
and given as a list of 8 components:
tau
Matrix with size
Q\times n
containing the estimated probability in(0,1)
that clusterq
contains nodei
.rho
Sparsity parameter - 1 in this case (non sparse method).
beta
Sparsity parameter - 1 in this case (non sparse method).
logintensities.ql
Matrix with size
Q(Q+1)/2\times K
. Each row contains estimated values of the log of the intensity function\log(\alpha^{(q,l)})
on a regular partition (inK
parts) of the time interval [0,Time].best.d
Vector with length
Q(Q+1)/2
(undirected case) with estimated value for the exponent of the best partition to estimate intensity\alpha^{(q,l)}
. The best number of parts isK=2^d
.J
Estimated value of the ELBO
run
Which run of the algorithm gave the best solution. A run relies on a specific initialization of the algorithm. A negative value maybe obtained in the decreasing phase (for Q) of the algorithm.
converged
Boolean. If TRUE, the algorithm stopped at convergence. Otherwise it stopped at the maximal number of iterations.
Details
This solution was (randomly) obtained using the following code
Nijk <- statistics(generated_Q3$data,n=50,K=8,directed=FALSE) generated_sol_hist <- mainVEM(list(Nijk=Nijk,Time=1),n=50,Qmin=1,Qmax=5,directed=FALSE,method='hist')
References
MATIAS, C., REBAFKA, T. & VILLERS, F. (2018). A semiparametric extension of the stochastic block model for longitudinal networks. Biometrika. 105(3): 665-680.