tidy.ergm: Tidy a(n) ergm object

View source: R/ergm-tidiers.R

tidy.ergmR Documentation

Tidy a(n) ergm object


Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

The methods should work with any model that conforms to the ergm class, such as those produced from weighted networks by the ergm.count package.


## S3 method for class 'ergm'
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = FALSE, ...)



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


Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.


The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.


Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.


Additional arguments to pass to ergm::summary(). Cautionary note: Misspecified arguments may be silently ignored.


A tibble::tibble with one row for each coefficient in the exponential random graph model, with columns:


The term in the model being estimated and tested


The estimated coefficient


The standard error


The MCMC error


The two-sided p-value


Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. Journal of Statistical Software, 24(3). https://www.jstatsoft.org/v24/i03/.

See Also

tidy(), ergm::ergm(), ergm::control.ergm(), ergm::summary()

Other ergm tidiers: glance.ergm()


# load libraries for models and data

# load the Florentine marriage network data

# fit a model where the propensity to form ties between
# families depends on the absolute difference in wealth
gest <- ergm(flomarriage ~ edges + absdiff("wealth"))

# show terms, coefficient estimates and errors

# show coefficients as odds ratios with a 99% CI
tidy(gest, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.99)

# take a look at likelihood measures and other
# control parameters used during MCMC estimation
glance(gest, deviance = TRUE)
glance(gest, mcmc = TRUE)

broom documentation built on Aug. 30, 2022, 1:07 a.m.