tidy.btergm {broom} | R Documentation |
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 class '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. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble with one row per term in the random graph model and columns:
term |
The term in the model being estimated and tested. |
estimate |
The estimated value of the coefficient. |
conf.low |
The lower bound of the confidence interval. |
conf.high |
The lower bound of the confidence interval. |
if (require("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) }