Glance accepts a model object and returns a
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
# S3 method for ergm glance(x, deviance = FALSE, mcmc = FALSE, ...)
Logical indicating whether or not to report null and
residual deviance for the model, as well as degrees of freedom. Defaults
Logical indicating whether or not to report MCMC interval,
burn-in and sample size used to estimate the model. Defaults to
Additional arguments to pass to
tibble::tibble() with exactly one row and columns:
Akaike's Information Criterion for the model.
Bayesian Information Criterion for the model.
Needs custom info.
Residual degrees of freedom for the model.
The number of iterations performed before convergence
The log-likelihood of the model. [stats::logLik()] may be a useful reference.
The burn-in period of the MCMC estimation
The interval used during MCMC estimation
The sample size used during MCMC estimation
Deviance of the null model.
The residual deviance of the model
Whether the model assumed dyadic independence