# Modification of add1.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, 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/
add1.gnm <- function (object, scope, scale = 0,
test = c("none", "Chisq", "F"), x = NULL, k = 2, ...)
{
if (any(attr(terms(object), "type") != "Linear"))
stop("add1 is not implemented for gnm objects with nonlinear terms.")
Fstat <- function(table, rdf) {
dev <- table$Deviance
df <- table$Df
diff <- pmax(0, (dev[1L] - dev)/df)
Fs <- (diff/df)/(dev/(rdf - df))
Fs[df < .Machine$double.eps] <- NA
P <- Fs
nnas <- !is.na(Fs)
P[nnas] <- pf(Fs[nnas], df[nnas], rdf - df[nnas],
lower.tail = FALSE)
list(Fs = Fs, P = P)
}
if (!is.character(scope))
scope <- add.scope(object, update.formula(object, scope))
if (!length(scope))
stop("no terms in scope for adding to object")
oTerms <- attr(object$terms, "term.labels")
int <- attr(object$terms, "intercept")
ns <- length(scope)
dfs <- dev <- numeric(ns + 1)
names(dfs) <- names(dev) <- c("<none>", scope)
add.rhs <- paste(scope, collapse = "+")
add.rhs <- eval(parse(text = paste("~ . +", add.rhs)))
new.form <- update.formula(object, add.rhs)
Terms <- terms(new.form)
y <- object$y
if (is.null(x)) {
fc <- object$call
fc$formula <- Terms
fob <- list(call = fc, terms = Terms)
class(fob) <- oldClass(object)
m <- model.frame(fob, xlev = object$xlevels)
offset <- model.offset(m)
wt <- model.weights(m)
x <- model.matrix(Terms, m, contrasts.arg = object$contrasts)
oldn <- length(y)
y <- model.response(m)
if (!is.factor(y))
storage.mode(y) <- "double"
if (NCOL(y) == 2) {
n <- y[, 1] + y[, 2]
y <- ifelse(n == 0, 0, y[, 1]/n)
if (is.null(wt))
wt <- rep.int(1, length(y))
wt <- wt * n
}
newn <- length(y)
if (newn < oldn)
warning(gettextf("using the %d/%d rows from a combined fit",
newn, oldn), domain = NA)
}
else {
wt <- object$prior.weights
offset <- object$offset
}
n <- nrow(x)
if (is.null(wt))
wt <- rep.int(1, n)
Terms <- attr(Terms, "term.labels")
asgn <- attr(x, "assign")
ousex <- match(asgn, match(oTerms, Terms), 0L) > 0L
if (int)
ousex[1L] <- TRUE
X <- x[, ousex, drop = FALSE]
z <- glm.fit.e(X, y, wt, offset = offset, family = object$family,
control = glm.control(object$tolerance, object$iterMax,
object$trace),
eliminate = object$eliminate)
dfs[1L] <- z$rank
dev[1L] <- z$deviance
sTerms <- vapply(strsplit(Terms, ":", fixed = TRUE),
function(x) paste(sort(x),
collapse = ":"), character(1))
for (tt in scope) {
stt <- paste(sort(strsplit(tt, ":")[[1L]]), collapse = ":")
usex <- match(asgn, match(stt, sTerms), 0L) > 0L
X <- x[, usex | ousex, drop = FALSE]
z <- glm.fit.e(X, y, wt, offset = offset, family = object$family,
control = glm.control(object$tolerance, object$iterMax,
object$trace),
eliminate = object$eliminate)
dfs[tt] <- z$rank
dev[tt] <- z$deviance
}
if (scale == 0)
dispersion <- summary(object, dispersion = NULL)$dispersion
else dispersion <- scale
fam <- object$family$family
if (fam == "gaussian") {
if (scale > 0)
loglik <- dev/scale - n
else loglik <- n * log(dev/n)
}
else loglik <- dev/dispersion
aic <- loglik + k * dfs
aic <- aic + (extractAIC(object, k = k)[2L] - aic[1L])
dfs <- dfs - dfs[1L]
dfs[1L] <- NA
aod <- data.frame(Df = dfs, Deviance = dev, AIC = aic,
row.names = names(dfs),
check.names = FALSE)
if (all(is.na(aic)))
aod <- aod[, -3]
test <- match.arg(test)
if (test == "Chisq") {
dev <- pmax(0, loglik[1L] - loglik)
dev[1L] <- NA
LRT <- if (dispersion == 1)
"LRT"
else "scaled dev."
aod[, LRT] <- dev
nas <- !is.na(dev)
dev[nas] <- pchisq(dev[nas], aod$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)
rdf <- object$df.residual
aod[, c("F value", "Pr(F)")] <- Fstat(aod, rdf)
}
head <- c("Single term additions", "\nModel:",
deparse(as.vector(formula(object))),
if (scale > 0) paste("\nscale: ", format(scale), "\n"))
class(aod) <- c("anova", "data.frame")
attr(aod, "heading") <- head
aod
}
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