Nothing
#' @templateVar class ergm
#' @template title_desc_tidy
#'
#' @description The methods should work with any model that conforms to
#' the \pkg{ergm} class, such as those produced from weighted networks by the
#' \pkg{ergm.count} package.
#'
#' @param x An `ergm` object returned from a call to [ergm::ergm()].
#' @template param_confint
#' @template param_exponentiate
#' @param ... Additional arguments to pass to [ergm::summary()].
#' **Cautionary note**: Misspecified arguments may be silently ignored.
#'
#'
#' @return A [tibble::tibble] with one row for each coefficient in the
#' exponential random graph model, with columns:
#' \item{term}{The term in the model being estimated and tested}
#' \item{estimate}{The estimated coefficient}
#' \item{std.error}{The standard error}
#' \item{mcmc.error}{The MCMC error}
#' \item{p.value}{The two-sided p-value}
#'
#' @examplesIf rlang::is_installed("ergm")
#'
#' # load libraries for models and data
#' library(ergm)
#'
#' # load the Florentine marriage network data
#' data(florentine)
#'
#' # fit a model where the propensity to form ties between
#' # families depends on the absolute difference in wealth
#' gest <- ergm(flomarriage ~ edges + absdiff("wealth"))
#'
#' # show terms, coefficient estimates and errors
#' tidy(gest)
#'
#' # show coefficients as odds ratios with a 99% CI
#' tidy(gest, exponentiate = TRUE, conf.int = TRUE, conf.level = 0.99)
#'
#' # take a look at likelihood measures and other
#' # control parameters used during MCMC estimation
#' glance(gest)
#' glance(gest, deviance = TRUE)
#' glance(gest, mcmc = TRUE)
#'
#' @references Hunter DR, Handcock MS, Butts CT, Goodreau SM, Morris M (2008b).
#' \pkg{ergm}: A Package to Fit, Simulate and Diagnose Exponential-Family
#' Models for Networks. *Journal of Statistical Software*, 24(3).
#' <https://www.jstatsoft.org/v24/i03/>.
#'
#' @export
#' @aliases ergm_tidiers
#' @seealso [tidy()], [ergm::ergm()], [ergm::control.ergm()],
#' [ergm::summary()]
#' @family ergm tidiers
tidy.ergm <- function(x, conf.int = FALSE, conf.level = 0.95,
exponentiate = FALSE, ...) {
# in ergm 3.9 summary(x, ...)$coefs has columns:
# Estimate, Std. Error, MCMC %, Pr(>|Z|)
# in ergm 3.10 summary(x, ...)$coefs has columns:
# Estimate, Std. Error, MCMC %, z value, Pr(>|Z|)
ret <- summary(x, ...)$coefficients %>%
tibble::as_tibble(rownames = "term") %>%
rename2(
term = "term",
estimate = "Estimate",
std.error = "Std. Error",
mcmc.error = "MCMC %",
statistic = "z value",
p.value = "Pr(>|z|)"
)
if (conf.int) {
z <- stats::qnorm(1 - (1 - conf.level) / 2)
ret$conf.low <- ret$estimate - z * ret$std.error
ret$conf.high <- ret$estimate + z * ret$std.error
}
if (exponentiate) {
if (is.null(x$glm) ||
(x$glm$family$link != "logit" && x$glm$family$link != "log")) {
warning("Exponentiating but model didn't use log or logit link.")
}
ret <- exponentiate(ret)
}
as_tibble(ret)
}
#' @templateVar class ergm
#' @template title_desc_glance
#'
#' @inheritParams tidy.ergm
#' @param deviance Logical indicating whether or not to report null and
#' residual deviance for the model, as well as degrees of freedom. Defaults
#' to `FALSE`.
#' @param mcmc Logical indicating whether or not to report MCMC interval,
#' burn-in and sample size used to estimate the model. Defaults to `FALSE`.
#'
#' @return `glance.ergm` returns a one-row tibble with the columns
#' \item{independence}{Whether the model assumed dyadic independence}
#' \item{iterations}{The number of MCMLE iterations performed before convergence}
#' \item{logLik}{If applicable, the log-likelihood associated with the model}
#' \item{AIC}{The Akaike Information Criterion}
#' \item{BIC}{The Bayesian Information Criterion}
#'
#' If `deviance = TRUE`, and if the model supports it, the
#' tibble will also contain the columns
#' \item{null.deviance}{The null deviance of the model}
#' \item{df.null}{The degrees of freedom of the null deviance}
#' \item{residual.deviance}{The residual deviance of the model}
#' \item{df.residual}{The degrees of freedom of the residual deviance}
#'
#' @export
#' @seealso [glance()], [ergm::ergm()], [ergm::summary.ergm()]
#' @family ergm tidiers
glance.ergm <- function(x, deviance = FALSE, mcmc = FALSE, ...) {
s <- summary(x, ...) # produces lots of messages
ret <- as_glance_tibble(
independence = s$independence,
iterations = x$iterations,
logLik = as.numeric(logLik(x)),
na_types = "lir"
)
if (deviance & !is.null(ret$logLik)) {
# see #567 for details on the following
if (utils::packageVersion("ergm") < "3.10") {
dyads <- sum(
ergm::as.rlebdm(x$constrained, x$constrained.obs, which = "informative")
)
} else {
dyads <- stats::nobs(x)
}
lln <- ergm::logLikNull(x)
ret$null.deviance <- if (is.na(lln)) 0 else -2 * lln
ret$df.null <- dyads
ret$residual.deviance <- -2 * ret$logLik
ret$df.residual <- dyads - length(x$coefs)
}
ret$AIC <- stats::AIC(x)
ret$BIC <- stats::BIC(x)
if (mcmc) {
if (isTRUE(x$MPLE_is_MLE)) {
message(
"Though `glance` was supplied `mcmc = TRUE`, the model was not fitted",
"using MCMC, so the corresponding columns will be omitted."
)
}
ret$MCMC.interval <- x$control$MCMC.interval
ret$MCMC.burnin <- x$control$MCMC.burnin
ret$MCMC.samplesize <- x$control$MCMC.samplesize
}
ret
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.