broom (version 1.0.4)

tidy.btergm: Tidy a(n) btergm object

Description

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.

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.

Usage

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

Value

A tibble::tibble() with columns:

conf.high

Upper bound on the confidence interval for the estimate.

conf.low

Lower bound on the confidence interval for the estimate.

estimate

The estimated value of the regression term.

term

The name of the regression term.

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.

...

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. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

See Also

tidy(), btergm::btergm()

Examples

if (FALSE) { # (rlang::is_installed("btergm") & rlang::is_installed("network"))

library(btergm)
library(network)

set.seed(5)

# 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(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 the model
mod <- btergm(networks ~ edges + istar(2) + edgecov(covariates), R = 100)

# summarize model fit with tidiers
tidy(mod)
}