\Header{glm}{Fitting Generalized Linear Models}
\methalias{glm.fit}{glm}
\alias{glm.fit.null}{glm}
\alias{weights.glm}{glm}
\keyword{models}{glm}
\keyword{regression}{glm}
\begin{Description}\relax
\code{glm} is used to fit generalized linear models, specified by
giving a symbolic description of the linear predictor and a
description of the error distribution.\end{Description}
\begin{Usage}
\begin{verbatim}
glm(formula, family = gaussian, data, weights = NULL, subset = NULL,
    na.action, start = NULL, offset = NULL,
    control = glm.control(...), model = TRUE, method = "glm.fit",
    x = FALSE, y = TRUE, contrasts = NULL, ...)

glm.fit(x, y, weights = rep(1, nrow(x)),
        start = NULL, etastart  =  NULL, mustart = NULL,
        offset = rep(0, nrow(x)),
        family = gaussian(), control = glm.control(),
        intercept = TRUE)
glm.fit.null(x, y, weights = rep(1, nrow(x)),
        start = NULL, etastart  =  NULL, mustart = NULL,
        offset = rep(0, nrow(x)),
        family = gaussian(), control = glm.control(),
        intercept = FALSE)
weights(object, type = c("prior", "working"), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{formula}] a symbolic description of the model to be fit.
The details of model specification are given below.
\item[\code{family}] a description of the error distribution and link
function to be used in the model.
See \code{\Link{family}} for details.
\item[\code{data}] an optional data frame containing the variables
in the model.  By default the variables are taken from
\code{environment(formula)}, typically the environment from which
\code{glm} is called.
\item[\code{weights}] an optional vector of weights to be used
in the fitting process.
\item[\code{subset}] an optional vector specifying a subset of observations
to be used in the fitting process.
\item[\code{na.action}] a function which indicates what should happen
when the data contain \code{NA}s.  The default is set by
the \code{na.action} setting of \code{\Link{options}}, and is
\code{\Link{na.fail}} if that is unset.  The ``factory-fresh''
default is \code{\Link{na.omit}}.
\item[\code{start}] starting values for the parameters in the linear predictor.
\item[\code{etastart}] starting values for the linear predictor.
\item[\code{mustart}] starting values for the vector of means.
\item[\code{offset}] this can be used to specify an \emph{a priori}
known component to be included in the linear predictor
during fitting.
\item[\code{control}] a list of parameters for controlling the fitting
process.  See the documentation for \code{\Link{glm.control}}
for details.
\item[\code{model}] a logical value indicating whether \emph{model frame}
should be included as a component of the returned value.
\item[\code{method}] the method to be used in fitting the model.
The default (and presently only) method \code{glm.fit}
uses iteratively reweighted least squares (IWLS).
\item[\code{x, y}] For \code{glm}:
logical values indicating whether the response
vector and model matrix used in the fitting process
should be returned as components of the returned value.

For \code{glm.fit}: \code{x} is a design matrix of dimension \code{n
      * p}, and \code{y} is a vector of observations of length \code{n}.

\item[\code{contrasts}] an optional list. See the \code{contrasts.arg}
of \code{model.matrix.default}.
\item[\code{object}] an object inheriting from class \code{"glm"}.
\item[\code{type}] character, partial matching allowed.  Type of weights to
extract from the fitted model object.
\item[\code{intercept}] logical. Should an intercept be included?
\item[\code{...}] further arguments passed to or from other methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
A typical predictor has the form \code{response \textasciitilde{} terms} where
\code{response} is the (numeric) response vector and \code{terms} is a
series of terms which specifies a linear predictor for \code{response}.
For \code{binomial} models the response can also be specified as a
\code{\Link{factor}} (when the first level denotes failure and all
others success) or as a two-column matrix with the columns giving the
numbers of successes and failures.  A terms specification of the form
\code{first + second} indicates all the terms in \code{first} together
with all the terms in \code{second} with duplicates removed.

A specification of the form \code{first:second} indicates the
the set of terms obtained by taking the interactions of
all terms in \code{first} with all terms in \code{second}.
The specification \code{first*second} indicates the \emph{cross}
of \code{first} and \code{second}.
This is the same as \code{first + second + first:second}.

\code{glm.fit} and \code{glm.fit.null} are the workhorse functions:
the former calls the latter for a null model (with no intercept).\end{Details}
\begin{Value}
\code{glm} returns an object of class \code{glm}
which inherits from the class \code{lm}. See later in this section.

The function \code{\Link{summary}} (i.e., \code{\Link{summary.glm}}) can
be used to obtain or print a summary of the results and the function
\code{\Link{anova}} (i.e., \code{\Link{anova.glm}})
to produce an analysis of variance table.

The generic accessor functions \code{\Link{coefficients}},
\code{effects}, \code{fitted.values} and \code{residuals} can be used to
extract various useful features of the value returned by \code{glm}.

\code{weights} extracts a vector of weights, one for each case in the
fit (after subsetting and \code{na.action}).

An object of class \code{"glm"} is a list containing at least the
following components:

\begin{ldescription}
\item[\code{coefficients}] a named vector of coefficients
\item[\code{residuals}] the \emph{working} residuals, that is the residuals
in the final iteration of the IWLS fit.
\item[\code{fitted.values}] the fitted mean values, obtained by transforming
the linear predictors by the inverse of the link function.
\item[\code{rank}] the numeric rank of the fitted linear model.
\item[\code{family}] the \code{\Link{family}} object used.
\item[\code{linear.predictors}] the linear fit on link scale.
\item[\code{deviance}] up to a constant, minus twice the maximized
log-likelihood.  Where sensible, the constant is chosen so that a
saturated model has deviance zero.
\item[\code{aic}] Akaike's \emph{An Information Criterion}, minus twice the
maximized log-likelihood plus twice the number of coefficients (so
assuming that the dispersion is known.
\item[\code{null.deviance}] The deviance for the null model, comparable with
\code{deviance}. The null model will include the offset, and an
intercept if there is one in the model
\item[\code{iter}] the number of iterations of IWLS used.
\item[\code{weights}] the \emph{working} weights, that is the weights
in the final iteration of the IWLS fit.
\item[\code{prior.weights}] the case weights initially supplied.
\item[\code{df.residual}] the residual degrees of freedom.
\item[\code{df.null}] the residual degrees of freedom for the null model.
\item[\code{y}] the \code{y} vector used. (It is a vector even for a binomial
model.)
\item[\code{converged}] logical. Was the IWLS algorithm judged to have converged?
\item[\code{boundary}] logical. Is the fitted value on the boundary of the
attainable values?
\item[\code{call}] the matched call.
\item[\code{formula}] the formula supplied.
\item[\code{terms}] the \code{\Link{terms}} object used.
\item[\code{data}] the \code{data argument}.
\item[\code{offset}] the offset vector used.
\item[\code{control}] the value of the \code{control} argument used.
\item[\code{method}] the name of the fitter function used, in \R{} always
\code{"glm.fit"}.
\item[\code{contrasts}] (where relevant) the contrasts used.
\item[\code{xlevels}] (where relevant) a record of the levels of the factors
used in fitting.
\end{ldescription}


In addition, non-null fits will have components \code{qr}, \code{R}
and \code{effects} relating to the final weighted linear fit.

Objects of class \code{"glm"} are normally of class \code{c("glm",
    "lm")}, that is inherit from class \code{"lm"}, and well-designed
methods for class \code{"lm"} will be applied to the weighted linear
model at the final iteration of IWLS.  However, care is needed, as
extractor functions for class \code{"glm"} such as
\code{\Link{residuals}} and \code{weights} do \bold{not} just pick out
the component of the fit with the same name.

If a \code{\Link{binomial}} \code{glm} model is specified by giving a
two-column response, the weights returned by \code{prior.weights} are
the total numbers of cases (factored by the supplied case weights) and
the component \code{y} of the result is the proportion of successes.\end{Value}
\begin{References}\relax
McCullagh P. and Nelder, J. A. (1989)
\emph{Generalized Linear Models.}
London: Chapman and Hall.

Dobson, A. J. (1990)
\emph{An Introduction to Generalized Linear Models.}
London: Chapman and Hall.

Venables, W. N. and Ripley, B. D. (1999)
\emph{Modern Applied Statistics with S-PLUS.}
New York: Springer.\end{References}
\begin{SeeAlso}\relax
\code{\Link{anova.glm}}, \code{\Link{summary.glm}}, etc. for
\code{glm} methods,
and the generic functions \code{\Link{anova}}, \code{\Link{summary}},
\code{\Link{effects}}, \code{\Link{fitted.values}},
and \code{\Link{residuals}}. Further, \code{\Link{lm}} for
non-generalized \emph{linear} models.

\code{\Link{esoph}}, \code{\Link{infert}} and
\code{\Link{predict.glm}} have examples of fitting binomial glms.\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
anova(glm.D93)
summary(glm.D93)

## an example with offsets from Venables & Ripley (1999, pp.217-8)


## Need the anorexia data from a recent version of the package MASS:
library(MASS)
data(anorexia)

anorex.1 <- glm(Postwt ~ Prewt + Treat + offset(Prewt),
            family = gaussian, data = anorexia)
summary(anorex.1)

# A Gamma example, from McCullagh & Nelder (1989, pp. 300-2)
clotting <- data.frame(
    u = c(5,10,15,20,30,40,60,80,100),
    lot1 = c(118,58,42,35,27,25,21,19,18),
    lot2 = c(69,35,26,21,18,16,13,12,12))
summary(glm(lot1 ~ log(u), data=clotting, family=Gamma))
summary(glm(lot2 ~ log(u), data=clotting, family=Gamma))


\end{ExampleCode}
\end{Examples}

