Tidy a(n) btergm object

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, ...)

Arguments

x	A btergm::btergm() object.
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 FALSE.
quick	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. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.lvel = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

Value

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.

See also

tidy(), btergm::btergm()

Examples

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)
}
#> Loading required package: xergm
#> Warning: there is no package called ‘xergm’