Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modelling
function. This includes the name of the modelling function or any
arguments passed to the modelling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

# S3 method for ergm
glance(x, deviance = FALSE, mcmc = FALSE, ...)

## Arguments

x |
An `ergm` object returned from a call to `ergm::ergm()` . |

deviance |
Logical indicating whether or not to report null and
residual deviance for the model, as well as degrees of freedom. Defaults
to `FALSE` . |

mcmc |
Logical indicating whether or not to report MCMC interval,
burn-in and sample size used to estimate the model. Defaults to `FALSE` . |

... |
Additional arguments to pass to `ergm::summary.ergm()` .
**Cautionary note**: Mispecified arguments may be silently ignored. |

## See also

## Value

A `tibble::tibble()`

with exactly one row and columns:

AICAkaike's Information Criterion for the model.

BICBayesian Information Criterion for the model.

df.nullNeeds custom info.

df.residualResidual degrees of freedom for the model.

iterationsThe number of iterations performed before convergence

logLikThe log-likelihood of the model. [stats::logLik()] may be a useful reference.

MCMC.burninThe burn-in period of the MCMC estimation

MCMC.intervalThe interval used during MCMC estimation

MCMC.samplesizeThe sample size used during MCMC estimation

null.devianceDeviance of the null model.

residual.devianceThe residual deviance of the model

independenceWhether the model assumed dyadic independence