R: Tidying method for a bootstrapped temporal exponential random...
btergm_tidiers
R Documentation
Tidying method for a bootstrapped temporal exponential random graph model
Description
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,
quick = FALSE, ...)
Arguments
x
a btergm object
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 term and estimate columns.
...
extra arguments (currently not used)
Details
There is no augment or glance method
for ergm objects.
Value
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
See Also
btergm
Examples
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)
}