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# Modification of drop1.glm from the stats package for R.
#
# Copyright (C) 1994-8 W. N. Venables and B. D. Ripley
# Copyright (C) 1998-2005 The R Core Team
# Copyright (C) 2005, 2010, 2013, 2023 Heather Turner
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 or 3 of the License
# (at your option).
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
drop1.gnm <- function (object, scope, scale = 0, test = c("none", "Chisq",
"F"), k = 2, ...)
{
if (any(attr(terms(object), "type") != "Linear"))
stop("add1 is not implemented for gnm objects with nonlinear terms.")
x <- model.matrix(object)
n <- nrow(x)
asgn <- attr(x, "assign")
tl <- attr(object$terms, "term.labels")
if (missing(scope))
scope <- drop.scope(object)
else {
if (!is.character(scope))
scope <- attr(terms(update.formula(object, scope)),
"term.labels")
if (!all(match(scope, tl, 0L) > 0L))
stop("scope is not a subset of term labels")
}
ndrop <- match(scope, tl)
ns <- length(scope)
rdf <- object$df.residual
chisq <- object$deviance
dfs <- numeric(ns)
dev <- numeric(ns)
y <- object$y
if (is.null(y)) {
y <- model.response(model.frame(object))
if (!is.factor(y))
storage.mode(y) <- "double"
}
wt <- object$prior.weights
if (is.null(wt))
wt <- rep.int(1, n)
for (i in 1L:ns) {
ii <- seq_along(asgn)[asgn == ndrop[i]]
jj <- setdiff(seq(ncol(x)), ii)
z <- glm.fit.e(x[, jj, drop = FALSE], y, wt, offset = object$offset,
family = object$family,
control = glm.control(object$tolerance, object$iterMax,
object$trace),
eliminate = object$eliminate)
dfs[i] <- z$rank
dev[i] <- z$deviance
}
scope <- c("<none>", scope)
dfs <- c(object$rank, dfs)
dev <- c(chisq, dev)
dispersion <- if (is.null(scale) || scale == 0)
summary(object, dispersion = NULL)$dispersion
else scale
fam <- object$family$family
loglik <- if (fam == "gaussian") {
if (scale > 0)
dev/scale - n
else n * log(dev/n)
}
else dev/dispersion
aic <- loglik + k * dfs
dfs <- dfs[1L] - dfs
dfs[1L] <- NA
aic <- aic + (extractAIC(object, k = k)[2L] - aic[1L])
aod <- data.frame(Df = dfs, Deviance = dev, AIC = aic, row.names = scope,
check.names = FALSE)
if (all(is.na(aic)))
aod <- aod[, -3]
test <- match.arg(test)
if (test == "Chisq") {
dev <- pmax(0, loglik - loglik[1L])
dev[1L] <- NA
nas <- !is.na(dev)
LRT <- if (dispersion == 1)
"LRT"
else "scaled dev."
aod[, LRT] <- dev
dev[nas] <- pchisq(dev[nas], aod$Df[nas], lower.tail = FALSE)
aod[, "Pr(Chi)"] <- dev
}
else if (test == "F") {
if (fam == "binomial" || fam == "poisson")
warning(gettextf("F test assumes 'quasi%s' family",
fam), domain = NA)
dev <- aod$Deviance
rms <- dev[1L]/rdf
dev <- pmax(0, dev - dev[1L])
dfs <- aod$Df
rdf <- object$df.residual
Fs <- (dev/dfs)/rms
Fs[dfs < 1e-04] <- NA
P <- Fs
nas <- !is.na(Fs)
P[nas] <- pf(Fs[nas], dfs[nas], rdf, lower.tail = FALSE)
aod[, c("F value", "Pr(F)")] <- list(Fs, P)
}
head <- c("Single term deletions", "\nModel:",
deparse(as.vector(formula(object))),
if (!is.null(scale) && scale > 0) paste("\nscale: ",
format(scale), "\n"))
class(aod) <- c("anova", "data.frame")
attr(aod, "heading") <- head
aod
}
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