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

x | An |
---|---|

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

conf.level | The confidence level to use for the confidence interval
if |

exponentiate | 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 |

... | Additional arguments to pass to |

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

`tidy()`

, `ergm::ergm()`

, `ergm::control.ergm()`

,
`ergm::summary()`

Other ergm tidiers:
`glance.ergm()`

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"))#> Warning: `set_attrs()` is deprecated as of rlang 0.3.0 #> This warning is displayed once per session.#>#>#>#>#>#>#>#> # A tibble: 2 x 5 #> term estimate std.error statistic p.value #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 edges -2.30 0.402 -5.73 0.0000000102 #> 2 absdiff.wealth 0.0155 0.00616 2.52 0.0117# 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 statistic p.value conf.low conf.high #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 edges 0.100 0.402 -5.73 0.0000000102 0.0355 0.282 #> 2 absdiff.wealth 1.02 0.00616 2.52 0.0117 1.00 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.#> # A tibble: 1 x 9 #> independence iterations logLik null.deviance df.null residual.devian… #> <lgl> <int> <dbl> <logLik> <dbl> <dbl> #> 1 TRUE 4 -51.0 166.3553 120 102. #> # … with 3 more variables: df.residual <dbl>, AIC <dbl>, BIC <dbl>#> # 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>