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 cross 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 ergm
tidy(x, conf.int = FALSE, conf.level = 0.95,
  exponentiate = FALSE, quick = 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.


Logical indiciating if the only the term and estimate columns should be returned. Often useful to avoid time consuming covariance and standard error calculations. Defaults to FALSE.


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


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). http://www.jstatsoft.org/v24/i03/.

See also


A tibble::tibble() with columns:


The upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.


The lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.


The estimated value of the regression term.


The MCMC error


The two-sided p-value associated with the observed statistic.


The standard error of the regression term.


The name of the regression term.


library(ergm) # Using the same example as the ergm package # Load the Florentine marriage network data data(florentine) # Fit a model where the propensity to form ties between # families depends on the absolute difference in wealth gest <- ergm(flomarriage ~ edges + absdiff("wealth"))
#> Evaluating log-likelihood at the estimate.
# Show terms, coefficient estimates and errors tidy(gest)
#> # A tibble: 2 x 5 #> term estimate std.error mcmc.error p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 edges -2.30 0.402 0 0.0000000791 #> 2 absdiff.wealth 0.0155 0.00616 0 0.0131
# Show coefficients as odds ratios with a 99% CI tidy(gest, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.99)
#> # A tibble: 2 x 7 #> term estimate std.error mcmc.error p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 edges 0.100 0.402 0 0.0000000791 0.0355 0.282 #> 2 absdiff.wealth 1.02 0.00616 0 0.0131 1.000 1.03
# Take a look at likelihood measures and other # control parameters used during MCMC estimation glance(gest)
#> # A tibble: 1 x 5 #> independence iterations logLik AIC BIC #> <lgl> <int> <dbl> <dbl> <dbl> #> 1 TRUE 4 -51.0 106. 112.
glance(gest, deviance = TRUE)
#> # A tibble: 1 x 9 #> independence iterations logLik null.deviance df.null residual.devian… #> <lgl> <int> <dbl> <dbl> <dbl> <dbl> #> 1 TRUE 4 -51.0 166. 120 102. #> # ... with 3 more variables: df.residual <dbl>, AIC <dbl>, BIC <dbl>
glance(gest, mcmc = TRUE)
#> # A tibble: 1 x 8 #> independence iterations logLik AIC BIC MCMC.interval MCMC.burnin #> <lgl> <int> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 TRUE 4 -51.0 106. 112. 1024 16384 #> # ... with 1 more variable: MCMC.samplesize <dbl>