Fitting Generalized Linear Models
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"), ...)
The details of model specification are given below. |
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.) |
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. |
process. Should be
NULL or a numeric vector. |
to be used in the fitting process. |
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.|
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
process. See the documentation for
glm.control for details. |
should be included as a component of the returned value. |
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. |
logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
x is a design matrix of dimension
* p, and
y is a vector of observations of length
|an object inheriting from class
extract from the fitted model object. |
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
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 duplicates removed. 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.
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
second. This is the same as
first + second + first:second.
glm.fit is the workhorse function.
If more than one of
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
mustart are evaluated in the same way as variables in
formula, that is first in
data and then in the environment of
glm returns an object of class inheriting from
“glm” which inherits from the class
“lm”. See later in this section.
The generic accessor functions
residuals can be used to extract various useful features of the value returned by
weights extracts a vector of weights, one for each case in the fit (after subsetting and
An object of class
“glm” is a list containing at least the following components:
|a named vector of coefficients|
in the final iteration of the IWLS fit. Since cases with zero weights are omitted, their working residuals are
the linear predictors by the inverse of the link function. |
|the numeric rank of the fitted linear model.|
|the linear fit on link scale.|
log-likelihood. Where sensible, the constant is chosen so that a saturated model has deviance zero. |
maximized log-likelihood plus twice the number of coefficients (so assuming that the dispersion is known). |
deviance. The null model will include the offset, and an intercept if there is one in the model |
|the number of iterations of IWLS used.|
in the final iteration of the IWLS fit. |
|the case weights initially supplied.|
|the residual degrees of freedom.|
|the residual degrees of freedom for the null model.|
|logical. Was the IWLS algorithm judged to have converged?|
attainable values? |
|the matched call.|
|the formula supplied.|
|the offset vector used.|
|the value of the
|(where relevant) the contrasts used.|
used in fitting. |
In addition, non-empty fits will have components
effects relating to the final weighted linear fit.
Objects of class
“glm” are normally of class
“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
weights do not just pick out the component of the fit with the same name.
glm model is 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.
The original 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).
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.
## 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. 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)) ## Not run: ## for an example of the use of a terms object as a formula demo(glm.vr) ## End(Not run)