btergm_tidiers {broom} | R Documentation |
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 of the bootstrapped interval |
exponentiate |
whether to exponentiate the coefficient estimates and confidence intervals |
quick |
whether to compute a smaller and faster version, containing
only the |
... |
extra arguments (currently not used) |
There is no augment
or glance
method
for ergm objects.
A data.frame
without rownames.
tidy.btergm
returns one row for each coefficient,
with four columns:
term |
The term in the model being estimated and tested |
estimate |
The estimated coefficient |
conf.low |
The lower bound of the confidence interval |
conf.high |
The lower bound of the confidence interval |
if (require("xergm")) { # 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 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) }