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.

This method tidies the coefficients of a bootstrapped temporal exponential random graph model estimated with the xergm. It simply returns the coefficients and their confidence intervals.

# S3 method for btergm tidy(x, conf.level = 0.95, exponentiate = FALSE, quick = FALSE, ...)

x | A |
---|---|

conf.level | Confidence level for confidence intervals. Defaults to 0.95. |

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 |

quick | Logical indiciating if the only the |

... | Additional arguments. Not used. Needed to match generic
signature only. |

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 name of the regression term.

library(xergm)#>#>#>#> #>#>#> #>#>#>#> #> #> #> #> #> #> #>#> #>#> #> #> #> #> #> #> #> #> #> #> #> #>#>#> #> #>#> #>#>#> #>#> #>#>#> #>#>#> Warning: replacing previous import ‘statnet.common::colMeans.mcmc.list’ by ‘ergm::colMeans.mcmc.list’ when loading ‘ergm.count’#> Warning: replacing previous import ‘statnet.common::sweep.mcmc.list’ by ‘ergm::sweep.mcmc.list’ when loading ‘ergm.count’#> Warning: replacing previous import ‘statnet.common::colMeans.mcmc.list’ by ‘ergm::colMeans.mcmc.list’ when loading ‘tergm’#> Warning: replacing previous import ‘statnet.common::sweep.mcmc.list’ by ‘ergm::sweep.mcmc.list’ when loading ‘tergm’#> Warning: replacing previous import ‘statnet.common::colMeans.mcmc.list’ by ‘ergm::colMeans.mcmc.list’ when loading ‘statnet’#> Warning: replacing previous import ‘statnet.common::sweep.mcmc.list’ by ‘ergm::sweep.mcmc.list’ when loading ‘statnet’#>#> #> #>#> Error: package ‘btergm’ could not be loadedset.seed(1) # Using the same simulated example as the xergm package # Create 10 random networks with 10 actors networks <- list() for(i in 1:10){ mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10) diag(mat) <- 0 nw <- network::network(mat) networks[[i]] <- nw } # Create 10 matrices as covariates covariates <- list() for (i in 1:10) { mat <- matrix(rnorm(100), nrow = 10, ncol = 10) covariates[[i]] <- mat } # Fit a model where the propensity to form ties depends # on the edge covariates, controlling for the number of # in-stars suppressWarnings(btfit <- btergm(networks ~ edges + istar(2) + edgecov(covariates), R = 100))#> Error in btergm(networks ~ edges + istar(2) + edgecov(covariates), R = 100): could not find function "btergm"#> Error in tidy(btfit): object 'btfit' not found# Show coefficients as odds ratios with a 99% CI tidy(btfit, exponentiate = TRUE, conf.level = 0.99)#> Error in tidy(btfit, exponentiate = TRUE, conf.level = 0.99): object 'btfit' not found