bccm.Rd
bccm is used to fit a block-constrained configuration model.
bccm( adj, labels, directed = NULL, selfloops = NULL, directedBlocks = FALSE, homophily = FALSE, inBlockOnly = FALSE, xi = NULL, regular = FALSE, ... ) # S3 method for bccm print(x, suppressCall = FALSE, ...)
adj | the adjacency matrix of the graph. |
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labels | vector or list. contains the vertex labels to generate the blocks in the bccm. In the case of bipartite graphs should be a list of two vectors, the first one with row labels and the second one with column labels. |
directed | a boolean argument specifying whether the graph is directed or not. |
selfloops | boolean argument specifying whether the model should incorporate selfloops. |
directedBlocks | boolean argument specifying whether the model should incorporate directed blocks. Default to FALSE. |
homophily | boolean argument specifying whether the model should fit only homophily blocks. Default to FALSE. |
inBlockOnly | boolean argument specifying whether the model should fit only blocks over the diagonal. Default to FALSE. |
xi | an optional matrix defining the combinatorial matrix of the model. |
regular | optional boolean, fit regular gnp model? if not specified chosen through lr.test. |
... | optional arguments to print or plot methods. |
x | object of class |
suppressCall | logical, indicating whether to print the call that generated x |
bccm returns an object of class 'bccm' and 'ghype'. 'bccm' objects expand 'ghype' objects incorporating the parameter estimates.
print
: Print method for elements of class 'bccm'
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bccm
data("vertexlabels","adj_karate") blockmodel <- bccm(adj = adj_karate, labels = vertexlabels, directed = FALSE, selfloops = FALSE) data('adj_karate') data('vertexlabels') bcc.model <- bccm(adj_karate, labels=vertexlabels, directed=FALSE, selfloops=FALSE) print(bcc.model)#> Call: #> bccm(adj = adj_karate, labels = vertexlabels, directed = FALSE, #> selfloops = FALSE) #> block ghype undirected , no selfloops #> 34 vertices, 231 edges #> Loglikelihood: #> -336.8385 #> df: 36 #> #> Coefficients: #> Estimate Std.Err t value Pr(>t) #> 1<->1 1.00000000 0.00000000 Inf < 2.2e-16 *** #> 1<->2 0.09044494 0.00021692 416.95 < 2.2e-16 *** #> 2<->2 0.91049902 0.00040434 2251.79 < 2.2e-16 *** #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1