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.
The version of glm supplied by the package mixlm
parses to glmer for mixed modelling.
glm(formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset,
control = list(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, contrasts = NULL, REML = TRUE, ...)glm returns an object of class inheriting from "glm"
which inherits from the class "lm". See later in this section.
If a non-standard method is used, the object will also inherit
from the class (if any) returned by that function.
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:
a named vector of coefficients
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.
the fitted mean values, obtained by transforming the linear predictors by the inverse of the link function.
the numeric rank of the fitted linear model.
the family object used.
the linear fit on link scale.
up to a constant, minus twice the maximized log-likelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero.
A version of Akaike's An Information Criterion,
minus twice the maximized log-likelihood plus twice the number of
parameters, computed by the aic component of the family.
For binomial and Poison families the dispersion is
fixed at one and the number of parameters is the number of
coefficients. For gaussian, Gamma and inverse gaussian families the
dispersion is estimated from the residual deviance, and the number
of parameters is the number of coefficients plus one. For a
gaussian family the MLE of the dispersion is used so this is a valid
value of AIC, but for Gamma and inverse gaussian families it is not.
For families fitted by quasi-likelihood the value is NA.
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.
the number of iterations of IWLS used.
the working weights, that is the weights in the final iteration of the IWLS fit.
the weights initially supplied, a vector of
1s if none were.
the residual degrees of freedom.
the residual degrees of freedom for the null model.
if requested (the default) the y vector
used. (It is a vector even for a binomial model.)
if requested, the model matrix.
if requested (the default), the model frame.
logical. Was the IWLS algorithm judged to have converged?
logical. Is the fitted value on the boundary of the attainable values?
the matched call.
the formula supplied.
the terms object used.
the data argument.
the offset vector used.
the value of the control argument used.
the name of the fitter function used, currently always
"glm.fit".
(where relevant) the contrasts used.
(where relevant) a record of the levels of the factors used in fitting.
(where relevant) information returned by
model.frame on the special handling of NAs.
In addition, non-empty 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 well-designed
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
two-column 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.
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’.
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.)
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.
an optional vector of ‘prior weights’ to be used
in the fitting process. Should be NULL or a numeric vector.
an optional vector specifying a subset of observations to be used in the fitting process.
a function which indicates what should happen
when the data contain NAs. The default is set by
the na.action setting of options, and is
na.fail if that is unset. The ‘factory-fresh’
default is na.omit. Another possible value is
NULL, no action. Value na.exclude can be useful.
starting values for the parameters in the linear predictor.
starting values for the linear predictor.
starting values for the vector of means.
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 equal to
the number of cases. One or more offset terms can be
included in the formula instead or as well, and if more than one is
specified their sum is used. See model.offset.
a list of parameters for controlling the fitting
process. For glm.fit this is passed to
glm.control.
a logical value indicating whether model frame should be included as a component of the returned value.
the method to be used in fitting the model. The default
method "glm.fit" uses iteratively reweighted least squares
(IWLS): the alternative "model.frame" returns the model frame
and does no fitting.
User-supplied fitting functions can be supplied either as a function
or a character string naming a function, with a function which takes
the same arguments as glm.fit. If specified as a character
string it is looked up from within the stats namespace.
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.
an optional list. See the contrasts.arg
of model.matrix.default.
is used to invoke restricted maximum likelihood (TRUE) or maximum likelihood (FALSE) estimation instead of least squares.
For glm: arguments to be used to form the default
control argument if it is not supplied directly.
For weights: further arguments passed to or from other methods.
The argument method serves two purposes. One is to allow the
model frame to be recreated with no fitting. The other is to allow
the default fitting function glm.fit to be replaced by a
function which takes the same arguments and uses a different fitting
algorithm. If glm.fit is supplied as a character string it is
used to search for a function of that name, starting in the
stats namespace.
The class of the object return by the fitter (if any) will be
prepended to the class returned by glm.
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 re-written by members of the R Core team.
The design was inspired by the S function of the same name described in Hastie & Pregibon (1992).
Mixed model additions by Kristian Hovde Liland.
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
two-column 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 any duplicates removed.
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.
The terms in the formula will be re-ordered so that main effects come
first, followed by the interactions, all second-order, all third-order
and so on: to avoid this pass a terms object as the formula.
Non-NULL weights can be used to indicate that different
observations have different dispersions (with the values in
weights being inversely proportional to the dispersions); or
equivalently, when the elements of weights are positive
integers \(w_i\), that each response \(y_i\) is the mean of
\(w_i\) unit-weight observations. For a binomial GLM prior weights
are used to give the number of trials when the response is the
proportion of successes: they would rarely be used for a Poisson GLM.
glm.fit is the workhorse function: it is not normally called
directly but can be more efficient where the response vector and
design matrix have already been calculated.
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.
For the background to warning messages about ‘fitted probabilities numerically 0 or 1 occurred’ for binomial GLMs, see Venables & Ripley (2002, pp. 197--8).
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 non-generalized linear models (which SAS
calls GLMs, for ‘general’ linear models).
loglin and loglm (package
MASS) for fitting log-linear 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)
# 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))
# Mixed model example
dataset <- data.frame(y=rnorm(8), x=factor(c(rep(1,4),rep(0,4))), z=factor(rep(c(1,0),4)))
if(require(lme4)){
GLM <- glm(y ~ x+r(z), family=gaussian(identity), data=dataset)
summary(GLM)
logLik(GLM)
Anova(GLM,type=3)
}
if (FALSE) {
## for an example of the use of a terms object as a formula
demo(glm.vr)
}Run the code above in your browser using DataLab