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 class 'btergm'
tidy(x, conf.level = 0.95, exponentiate = 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.- ...
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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
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.
Examples
library(btergm)
#> Package: btergm
#> Version: 1.10.12
#> Date: 2024-03-31
#> Authors: Philip Leifeld (University of Manchester)
#> Skyler J. Cranmer (The Ohio State University)
#> Bruce A. Desmarais (Pennsylvania State University)
library(network)
#>
#> ‘network’ 1.18.2 (2023-12-04), 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
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)
#>
#> 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.
# summarize model fit with tidiers
tidy(mod)
#> # 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