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, ...)

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 |

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

A `tibble::tibble()`

with columns:

Upper bound on the confidence interval for the estimate.

Lower bound on the confidence interval for the estimate.

The estimated value of the regression term.

The name of the regression term.

if (FALSE) { library(xergm) set.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)) # Show terms, coefficient estimates and errors tidy(btfit) # Show coefficients as odds ratios with a 99% CI tidy(btfit, exponentiate = TRUE, conf.level = 0.99) }