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# File R/print.summary.ergm.R in package ergm, part of the
# Statnet suite of packages for network analysis, https://statnet.org .
#
# This software is distributed under the GPL-3 license. It is free,
# open source, and has the attribution requirements (GPL Section 7) at
# https://statnet.org/attribution .
#
# Copyright 2003-2023 Statnet Commons
################################################################################
#' @rdname summary.ergm
#' @order 2
#'
#' @param x object of class `summary.ergm` returned by [summary.ergm()].
#' @param digits significant digits for coefficients
#' @param signif.stars whether to print dots and stars to signify
#' statistical significance. See [print.summary.lm()].
#' @param eps.Pvalue \eqn{p}-values below this level will be printed
#' as "<`eps.Pvalue`".
#' @param
#' print.formula,print.fitinfo,print.coefmat,print.message,print.deviances,print.drop,print.offset,print.call
#' which components of the fit summary to print.
#'
#' @details The default printout of the summary object contains the
#' call, number of iterations used, null and residual deviances, and
#' the values of AIC and BIC (and their MCMC standard errors, if
#' applicable). The coefficient table contains the following
#' columns:
#'
#' - `Estimate`, `Std. Error` - parameter estimates and their standard errors
#' - `MCMC %` - if `total.variation=TRUE` (default) the percentage of standard
#' error attributable to MCMC estimation process rounded to an integer. See
#' also [vcov.ergm()] and its `sources` argument.
#' - `z value`, `Pr(>|z|)` - z-test and p-values
#'
#' @export
print.summary.ergm <- function (x,
digits = max(3, getOption("digits") - 3),
correlation=x$correlation, covariance=x$covariance,
signif.stars= getOption("show.signif.stars"),
eps.Pvalue=0.0001, print.formula=FALSE, print.fitinfo=TRUE, print.coefmat=TRUE, print.message=TRUE, print.deviances=TRUE, print.drop=TRUE, print.offset=TRUE, print.call=TRUE,...){
control <- x$control
# The following code is based on stats:::print.lm(), but there really isn't another concise way to do this:
if(print.call && !is.null(x$call)) cat("Call:\n", paste(deparse(x$call), sep="\n", collapse="\n"), "\n\n", sep="")
if(print.formula) cat("Formula:\n", paste(deparse(x$formula), sep="\n", collapse="\n"), "\n\n", sep="")
if(print.fitinfo){
## if (!is.null(x$iterations)) {
## cat("Iterations: ", x$iterations, "\n")
## }
cat(paste0(x$estimate.desc, " Results:\n\n"))
}
if(print.coefmat){
printCoefmat(coef(x), digits=digits, signif.stars=signif.stars,
P.values=TRUE, has.Pvalue=TRUE, na.print="NA",
eps.Pvalue=eps.Pvalue, cs.ind=1:2, tst.ind=4L,...)
}
if(print.message){
if(!is.null(x$message)){
cat(x$message)
}
cat("\n")
}
if(print.deviances){
if(is.null(x$devtable)) message(NO_LOGLIK_MESSAGE)
else if(length(x$devtable)>1 || !is.na(x$devtable)){
cat(c("",apply(cbind(paste(format(c(" Null", "Residual"), width = 8), x$devtext),
format(x$devtable[,1], digits = digits), " on",
format(x$devtable[,2], digits = digits)," degrees of freedom\n"),
1, paste, collapse = " "),"\n"))
if(x$null.lik.0) writeLines(c(strwrap(paste("Note that the null model likelihood and deviance are defined to be 0.", NO_NULL_IMPLICATION)),''))
cat(paste0("AIC: ", format(x$aic, digits = digits), " ",
"BIC: ", format(x$bic, digits = digits), " ",
"(Smaller is better. MC Std. Err. = ", format(sqrt(NVL(attr(x$aic,"vcov"),0)), digits=digits), ")", "\n"))
}
}
if(print.drop){
if(any(x$drop!=0)){
cat("\n Warning: The following terms have infinite coefficient estimates:\n ")
cat(rownames(coef(x))[x$drop!=0], "\n")
}
if(any(!x$estimable)){
cat("\n Warning: The following terms could not be estimated because they conflicted with the sample space constraint:\n ")
cat(rownames(coef(x))[!x$estimable], "\n")
}
}
if(print.offset){
if(any(x$offset & x$drop==0 & x$estimable)){
cat("\n The following terms are fixed by offset and are not estimated:\n ")
cat(rownames(coef(x))[x$offset & x$drop==0 & x$estimable], "\n\n")
}
}
if(covariance == TRUE){
cat("Asymptotic covariance matrix:\n")
print(x$asycov)
}
if(correlation == TRUE){
cat("\nAsymptotic correlation matrix:\n")
asycor <- x$asycov / crossprod(x$asyse)
dimnames(asycor) <- dimnames(x$asycov)
print(asycor)
}
invisible(x)
}
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