glm {stats}  R Documentation 
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.
glm(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, ...) glm.fit(x, y, weights = rep(1, nobs), start = NULL, etastart = NULL, mustart = NULL, offset = rep(0, nobs), family = gaussian(), control = glm.control(), intercept = TRUE) ## S3 method for class 'glm': weights(object, type = c("prior", "working"), ...)
formula 
an object of class "formula" (or one that
can be coerced to that class): a symbolic description of the
model to be fitted. The details of model specification are given
under Details. 
family 
a description of the error distribution and link
function to be used in the model. This can be a character string
naming a family function, a family function or the result of a call
to a family function. (See family for details of
family functions.) 
data 
an optional data frame, list or environment (or object
coercible by as.data.frame to a data frame) containing
the variables in the model. If not found in data , the
variables are taken from environment(formula) ,
typically the environment from which glm is called. 
weights 
an optional vector of weights to be used in the fitting
process. Should be NULL or a numeric vector. 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
na.action 
a function which indicates what should happen
when the data contain NA s. The default is set by
the na.action setting of options , and is
na.fail if that is unset. The “factoryfresh”
default is na.omit . Another possible value is
NULL , no action. Value na.exclude can be useful. 
start 
starting values for the parameters in the linear predictor. 
etastart 
starting values for the linear predictor. 
mustart 
starting values for the vector of means. 
offset 
this can be used to specify an a priori
known component to be included in the linear predictor
during fitting. This should be NULL or a numeric vector of
length either one or equal to the number of cases.
One or more offset terms can be included in the
formula instead or as well, and if both are specified their sum is
used. See model.offset . 
control 
a list of parameters for controlling the fitting
process. See the documentation for glm.control
for details. 
model 
a logical value indicating whether model frame should be included as a component of the returned value. 
method 
the method to be used in fitting the model.
The default method "glm.fit" uses iteratively reweighted
least squares (IWLS). The only current alternative is
"model.frame" which returns the model frame and does no fitting. 
x, y 
For 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 glm.fit : x is a design matrix of dimension n
* p , and y is a vector of observations of length n .

contrasts 
an optional list. See the contrasts.arg
of model.matrix.default . 
object 
an object inheriting from class "glm" . 
type 
character, partial matching allowed. Type of weights to extract from the fitted model object. 
intercept 
logical. Should an intercept be included in the null model? 
... 
further arguments passed to or from other methods. 
A typical predictor has the form response ~ terms
where
response
is the (numeric) response vector and terms
is a
series of terms which specifies a linear predictor for response
.
For binomial
and quasibinomial
families the response can
also be specified as a factor
(when the first level
denotes failure and all
others success) or as a twocolumn matrix with the columns giving the
numbers of successes and failures. A terms specification of the form
first + second
indicates all the terms in first
together
with all the terms in second
with duplicates removed. The terms in
the formula will be reordered so that main effects come first,
followed by the interactions, all secondorder, all thirdorder and so
on: to avoid this pass a terms
object as the formula.
A specification of the form first:second
indicates the
the set of terms obtained by taking the interactions of
all terms in first
with all terms in second
.
The specification first*second
indicates the cross
of first
and second
.
This is the same as first + second + first:second
.
glm.fit
is the workhorse function.
If more than one of etastart
, start
and mustart
is specified, the first in the list will be used. It is often
advisable to supply starting values for a quasi
family,
and also for families with unusual links such as gaussian("log")
.
All of weights
, subset
, offset
, etastart
and mustart
are evaluated in the same way as variables in
formula
, that is first in data
and then in the
environment of formula
.
glm
returns an object of class inheriting from "glm"
which inherits from the class "lm"
. See later in this section.
The function summary
(i.e., summary.glm
) can
be used to obtain or print a summary of the results and the function
anova
(i.e., anova.glm
)
to produce an analysis of variance table.
The generic accessor functions coefficients
,
effects
, fitted.values
and residuals
can be used to
extract various useful features of the value returned by glm
.
weights
extracts a vector of weights, one for each case in the
fit (after subsetting and na.action
).
An object of class "glm"
is a list containing at least the
following components:
coefficients 
a named vector of coefficients 
residuals 
the working residuals, that is the residuals
in the final iteration of the IWLS fit. Since cases with zero
weights are omitted, their working residuals are NA . 
fitted.values 
the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function. 
rank 
the numeric rank of the fitted linear model. 
family 
the family object used. 
linear.predictors 
the linear fit on link scale. 
deviance 
up to a constant, minus twice the maximized loglikelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero. 
aic 
Akaike's An Information Criterion, minus twice the maximized loglikelihood plus twice the number of coefficients (so assuming that the dispersion is known). 
null.deviance 
The deviance for the null model, comparable with
deviance . The null model will include the offset, and an
intercept if there is one in the model. Note that this will be
incorrect if the link function depends on the data other than
through the fitted mean: specify a zero offset to force a correct
calculation. 
iter 
the number of iterations of IWLS used. 
weights 
the working weights, that is the weights in the final iteration of the IWLS fit. 
prior.weights 
the case weights initially supplied. 
df.residual 
the residual degrees of freedom. 
df.null 
the residual degrees of freedom for the null model. 
y 
the y vector used. (It is a vector even for a binomial
model.) 
converged 
logical. Was the IWLS algorithm judged to have converged? 
boundary 
logical. Is the fitted value on the boundary of the attainable values? 
call 
the matched call. 
formula 
the formula supplied. 
terms 
the terms object used. 
data 
the data argument . 
offset 
the offset vector used. 
control 
the value of the control argument used. 
method 
the name of the fitter function used, currently always
"glm.fit" . 
contrasts 
(where relevant) the contrasts used. 
xlevels 
(where relevant) a record of the levels of the factors used in fitting. 
In addition, nonempty fits will have components qr
, R
and effects
relating to the final weighted linear fit.
Objects of class "glm"
are normally of class c("glm",
"lm")
, that is inherit from class "lm"
, and welldesigned
methods for class "lm"
will be applied to the weighted linear
model at the final iteration of IWLS. However, care is needed, as
extractor functions for class "glm"
such as
residuals
and weights
do not just pick out
the component of the fit with the same name.
If a binomial
glm
model was specified by giving a
twocolumn response, the weights returned by prior.weights
are
the total numbers of cases (factored by the supplied case weights) and
the component y
of the result is the proportion of successes.
The original R implementation of glm
was written by Simon
Davies working for Ross Ihaka at the University of Auckland, but has
since been extensively rewritten by members of the R Core team.
The design was inspired by the S function of the same name described in Hastie & Pregibon (1992).
Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.
Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.
anova.glm
, summary.glm
, etc. for
glm
methods,
and the generic functions anova
, summary
,
effects
, fitted.values
,
and residuals
.
lm
for nongeneralized linear models (which SAS
calls GLMs, for ‘general’ linear models).
loglin
and loglm
for fitting
loglinear models (which binomial and Poisson GLMs are) to contingency
tables.
bigglm
in package biglm for an alternative
way to fit GLMs to large datasets (especially those with many cases).
esoph
, infert
and
predict.glm
have examples of fitting binomial glms.
## 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 (2002, p.189) data(anorexia, package="MASS") anorex.1 < glm(Postwt ~ Prewt + Treat + offset(Prewt), family = gaussian, data = anorexia) summary(anorex.1) # A Gamma example, from McCullagh & Nelder (1989, pp. 3002) 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)) ## Not run: ## for an example of the use of a terms object as a formula demo(glm.vr) ## End(Not run)