#' @S3method summary netglm
summary.netglm <- function (
object,
dispersion = NULL,
correlation = FALSE,
symbolic.cor = FALSE,
...
) {
est.disp <- FALSE
df.r <- object$df.residual
if (is.null(dispersion))
dispersion <- if (object$family$family %in% c("poisson",
"binomial"))
1
else if (df.r > 0) {
est.disp <- TRUE
if (any(object$weights == 0))
warning("observations with zero weight not used for calculating dispersion")
sum((object$weights * object$residuals^2)[object$weights >
0])/df.r
}
else {
est.disp <- TRUE
NaN
}
aliased <- is.na(coef(object))
p <- object$rank
if (p > 0) {
p1 <- 1:p
Qr <- object$qr
coef.p <- object$coefficients[Qr$pivot[p1]]
names(coef.p) <- object$names
covmat.unscaled <- chol2inv(Qr$qr[p1, p1, drop = FALSE])
dimnames(covmat.unscaled) <- list(names(coef.p), names(coef.p))
covmat <- dispersion * covmat.unscaled
var.cf <- diag(covmat)
s.err <- sqrt(var.cf)
tvalue <- coef.p/s.err
dn <- c("Estimate", "Std. Error")
if (!est.disp) {
pvalue <- 2 * pnorm(-abs(tvalue))
coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
dimnames(coef.table) <- list(names(coef.p), c(dn,
"z value", "Pr(>|z|)"))
}
else if (df.r > 0) {
pvalue <- 2 * pt(-abs(tvalue), df.r)
coef.table <- cbind(coef.p, s.err, tvalue, pvalue)
dimnames(coef.table) <- list(names(coef.p), c(dn,
"t value", "Pr(>|t|)"))
}
else {
coef.table <- cbind(coef.p, NaN, NaN, NaN)
dimnames(coef.table) <- list(names(coef.p), c(dn,
"t value", "Pr(>|t|)"))
}
df.f <- NCOL(Qr$qr)
}
else {
coef.table <- matrix(, 0, 4)
dimnames(coef.table) <- list(NULL, c("Estimate", "Std. Error",
"t value", "Pr(>|t|)"))
covmat.unscaled <- covmat <- matrix(, 0, 0)
df.f <- length(aliased)
}
ans <- c(object[c("call", "terms", "family", "deviance",
"aic", "contrasts", "df.residual", "null.deviance", "df.null",
"iter", "na.action")], list(deviance.resid = residuals(object,
type = "deviance"), coefficients = coef.table, aliased = aliased,
dispersion = dispersion, df = c(object$rank, df.r, df.f),
cov.unscaled = covmat.unscaled, cov.scaled = covmat))
if (correlation && p > 0) {
dd <- sqrt(diag(covmat.unscaled))
ans$correlation <- covmat.unscaled/outer(dd, dd)
ans$symbolic.cor <- symbolic.cor
}
class(ans) <- "summary.glm"
return(ans)
}
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