Glance at a(n) ergm object
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 modeling function. This includes the name of the modeling function or any arguments passed to the modeling 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.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
## S3 method for class 'ergm' glance(x, deviance = FALSE, mcmc = FALSE, ...)
x |
An |
deviance |
Logical indicating whether or not to report null and
residual deviance for the model, as well as degrees of freedom. Defaults
to |
mcmc |
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 |
glance.ergm returns a one-row tibble with the columns
independence |
Whether the model assumed dyadic independence |
iterations |
The number of MCMLE iterations performed before convergence |
logLik |
If applicable, the log-likelihood associated with the model |
AIC |
The Akaike Information Criterion |
BIC |
The Bayesian Information Criterion |
If deviance = TRUE, and if the model supports it, the
tibble will also contain the columns
null.deviance |
The null deviance of the model |
df.null |
The degrees of freedom of the null deviance |
residual.deviance |
The residual deviance of the model |
df.residual |
The degrees of freedom of the residual deviance |
Other ergm tidiers:
tidy.ergm()
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