Nothing
glm <- function (formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset, control = glm.control(...),
model = TRUE, method = "glm.fit", x = FALSE, y = TRUE, contrasts = NULL, ...,
separation = c("find", "test") )
{
call <- match.call()
separation <- match.arg(separation)
if (is.character(family))
family <- get(family, mode = "function", envir = parent.frame())
if (is.function(family))
family <- family()
if (is.null(family$family)) {
print(family)
stop("'family' not recognized")
}
if (missing(data))
data <- environment(formula)
mf <- match.call(expand.dots = FALSE)
m <- match(c("formula", "data", "subset", "weights", "na.action",
"etastart", "mustart", "offset"), names(mf), 0)
mf <- mf[c(1, m)]
mf$drop.unused.levels <- TRUE
mf[[1]] <- as.name("model.frame")
mf <- eval(mf, parent.frame())
switch(method, model.frame = return(mf), glm.fit = 1, stop("invalid 'method': ", method))
mt <- attr(mf, "terms")
Y <- model.response(mf, "any")
if (length(dim(Y)) == 1) {
nm <- rownames(Y)
dim(Y) <- NULL
if (!is.null(nm))
names(Y) <- nm
}
X <- if (!is.empty.model(mt))
model.matrix(mt, mf, contrasts)
else matrix(, NROW(Y), 0)
weights <- as.vector(model.weights(mf))
if (!is.null(weights) && !is.numeric(weights))
stop("'weights' must be a numeric vector")
offset <- as.vector(model.offset(mf))
if (!is.null(weights) && any(weights < 0))
stop("negative weights not allowed")
if (!is.null(offset)) {
if (length(offset) == 1)
offset <- rep(offset, NROW(Y))
else if (length(offset) != NROW(Y))
stop(gettextf("number of offsets is %d should equal %d (number of observations)",
length(offset), NROW(Y)), domain = NA)
}
mustart <- model.extract(mf, "mustart")
etastart <- model.extract(mf, "etastart")
if(casefold(family$family) == "binomial" && length(unique(Y)) == 2) {
if(separation == "test") {
separation <- separator(X, Y, purpose = "test")$separation
#separation <- separationTest(X, Y)
if(separation)
stop("Separation exists among the sample points.\n\tThis model cannot be fit by maximum likelihood.")
}
if(separation == "find") {
separation <- separator(X, Y, purpose = "find")$beta
#separation <- separationDirection(X, Y)
separating.terms <- dimnames(X)[[2]][abs(separation) > 1e-09]
if(length(separating.terms))
stop(paste("The following terms are causing separation among the sample points:",
paste(separating.terms, collapse = ", ")))
}
}
fit <- glm.fit(x = X, y = Y, weights = weights, start = start,
etastart = etastart, mustart = mustart, offset = offset,
family = family, control = control, intercept = attr(mt,
"intercept") > 0)
if (length(offset) && attr(mt, "intercept") > 0) {
fit$null.deviance <- glm.fit(x = X[, "(Intercept)", drop = FALSE],
y = Y, weights = weights, offset = offset, family = family,
control = control, intercept = TRUE)$deviance
}
if (model)
fit$model <- mf
fit$na.action <- attr(mf, "na.action")
if (x)
fit$x <- X
if (!y)
fit$y <- NULL
fit <- c(fit, list(call = call, formula = formula, terms = mt,
data = data, offset = offset, control = control, method = method,
contrasts = attr(X, "contrasts"), xlevels = .getXlevels(mt, mf)))
class(fit) <- c("glm", "lm")
fit
}
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