General method to compute log-likelihood for ghype models.

logl(
object,
xi = NULL,
omega = NULL,
directed = NULL,
selfloops = NULL,
multinomial = NULL,
...
)

# S3 method for ghype
logl(
object,
xi = NULL,
omega = NULL,
directed = NULL,
selfloops = NULL,
multinomial = NULL,
...
)

# S3 method for matrix
logl(
object,
xi = NULL,
omega = NULL,
directed = NULL,
selfloops = NULL,
multinomial = NULL,
...
)

Arguments

object either an adjacency matrix or ghype model If a ghype model is passed, then xi, omega, directed, selfloops are ignored If an adjacency matrix is passed, then adj is ignored matrix, combinatorial matrix to build ghype model, considered only if object is an adjacency matrix matrix, propensity matrix to build ghype model, considered only if object is an adjacency matrix boolean, is ghype model directed? considered only if object is an adjacency matrix boolean, has ghype model selfloops? considered only if object is an adjacency matrix optional matrix, adjacency matrix of which to compute log-likelihood, cconsidered only if object is ghype model If adj is not passed, and object is a ghype model, the log-likelihood is computed for the original adjacency matrix stored in object. optional boolean. Force multinomial approximation? If not chosen, multinomial chosen for large graphs. additional parameters passed to and from internal methods

Value

loglikelihood value

Methods (by class)

• ghype: Computes log-likelihood for ghype models from model object

• matrix: Computes log-likelihood for ghype models from adjacency.

Examples

data('adj_karate')
model <- scm(adj_karate, FALSE, FALSE)
logl(object = model)#> [1] -434.5449new_adj <- adj_karate