Description Usage Arguments Value Examples

View source: R/user_function.R

The inference use a greedy algorithm to navigate between model size. For a given model size, the inference is done via a variational EM algorithm. The returned model is the one with the highest ICL criterion among all visited models.

By default the algorithm fits a single level SBM for each level, before
inferring the multilevel network. This step can be skipped by specifying an
initial clustering with the `init_clustering`

. Also, a given model size
can be force by setting the parameters `nb_clusters`

to a given value.

1 2 3 4 5 6 | ```
mlvsbm_estimate_network(
mlv,
nb_clusters = NULL,
init_clustering = NULL,
nb_cores = NULL
)
``` |

`mlv` |
A MLVSBM object, the network to be inferred |

`nb_clusters` |
A list of 2 integers, the model size.
If left to |

`init_clustering` |
A list of 2 vectors of integers of the same length as the number of node of each level. If specified, the algorithm will start from this clustering, then navigate freely. |

`nb_cores` |
An integer, the number of cores to use. Default to |

A FitMLVSBM object, the best inference of the network

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
my_mlvsbm <- MLVSBM::mlvsbm_simulate_network(
n = list(I = 10, O = 20), # Number of nodes for the lower level and the upper level
Q = list(I = 2, O = 2), # Number of blocks for the lower level and the upper level
pi = c(.3, .7), # Block proportion for the upper level, must sum to one
gamma = matrix(c(.9, .2, # Block proportion for the lower level,
.1, .8), # each column must sum to one
nrow = 2, ncol = 2, byrow = TRUE),
alpha = list(I = matrix(c(.8, .2,
.2, .1),
nrow = 2, ncol = 2, byrow = TRUE), # Connection matrix
O = matrix(c(.99, .3,
.3, .1),
nrow = 2, ncol = 2, byrow = TRUE)),# between blocks
directed = list(I = FALSE, O = FALSE), # Are the upper and lower level directed or not ?
affiliation = "preferential") # How the affiliation matrix is generated
fit <- MLVSBM::mlvsbm_estimate_network(mlv = my_mlvsbm, nb_cores = 1)
``` |

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