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.
# S3 method for btergm
tidy(x, conf.level = 0.95, exponentiate = FALSE, ...)A btergm::btergm() object.
Confidence level for confidence intervals. Defaults to 0.95.
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. 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.
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.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("bbmle", quietly = TRUE)) {
if (requireNamespace("network", quietly = TRUE)) {
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)
}
}
#> Registered S3 methods overwritten by 'ergm':
#> method from
#> simulate.formula lme4
#> simulate.formula_lhs lme4
#> Registered S3 methods overwritten by 'btergm':
#> method from
#> plot.gof ergm
#> print.gof ergm
#> Package: btergm
#> Version: 1.10.6
#> Date: 2022-04-01
#> Authors: Philip Leifeld (University of Essex)
#> Skyler J. Cranmer (The Ohio State University)
#> Bruce A. Desmarais (Pennsylvania State University)
#>
#> ‘network’ 1.17.1 (2021-06-12), part of the Statnet Project
#> * ‘news(package="network")’ for changes since last version
#> * ‘citation("network")’ for citation information
#> * ‘https://statnet.org’ for help, support, and other information
#>
#> Initial dimensions of the network and covariates:
#> t=1 t=2 t=3 t=4 t=5 t=6 t=7 t=8 t=9 t=10
#> networks (row) 10 10 10 10 10 10 10 10 10 10
#> networks (col) 10 10 10 10 10 10 10 10 10 10
#> covariates (row) 10 10 10 10 10 10 10 10 10 10
#> covariates (col) 10 10 10 10 10 10 10 10 10 10
#>
#> All networks are conformable.
#>
#> Dimensions of the network and covariates after adjustment:
#> t=1 t=2 t=3 t=4 t=5 t=6 t=7 t=8 t=9 t=10
#> networks (row) 10 10 10 10 10 10 10 10 10 10
#> networks (col) 10 10 10 10 10 10 10 10 10 10
#> covariates (row) 10 10 10 10 10 10 10 10 10 10
#> covariates (col) 10 10 10 10 10 10 10 10 10 10
#>
#> Starting pseudolikelihood estimation with 100 bootstrapping replications on a single computing core...
#> Done.
#> # A tibble: 3 × 4
#> term estimate conf.low conf.high
#> <chr> <dbl> <dbl> <dbl>
#> 1 edges -1.23 -1.37 -1.01
#> 2 istar2 0.0837 -0.0571 0.165
#> 3 edgecov.covariates[[i]] -0.0345 -0.177 0.112