| glm {base} | R Documentation |
glm is used to fit generalized linear models.
Models for glm are specified by giving
a symbolic description of the linear predictor and
a description of the error distribution.
A typical predictor has the form
reponse ~ terms where response is the (numeric)
response vector and terms is a series of terms which
specifies a linear predictor for response.
A terms specification of the form first+second
indicates all the terms in first together
with all the terms in second with 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.
glm(formula, family=gaussian, data, weights, subset,
na.action=na.fail, start=NULL, offset=NULL,
control=glm.control(epsilon=0.0001, maxit=10,
trace=F),
model=T, method=glm.fit, x=F, y=T)
summary(glm.obj, dispersion=NULL, correlation=TRUE,
na.action=na.omit)
anova(glm.obj, ...)
coefficients(glm.obj)
deviance(glm.obj)
df.residual(glm.obj)
effects(glm.obj)
family(glm.obj)
fitted.values(glm.obj)
residuals(glm.obj, type="deviance")
glm.control(epsilon=0.0001, maxit=10, trace=FALSE)
glm.fit(x, y, weights=rep(1, nrow(x)),
start=NULL, offset=rep(0, nrow(x)),
family=gaussian(), control=glm.control(),
intercept=TRUE)
formula |
a symbolic description of the model to be fit. The details of model specification are given below. |
family |
a description of the error distribution and link
function to be used in the model.
See |
data |
an optional data frame containing the variables
in the model. By default the variables are taken from
the environment which |
weights |
an optional vector of weights to be used in the fitting process. |
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 |
start |
starting values for the parameters in the linear predictor. |
offset |
this can be used to specify an a-priori known component to be included in the linear predictor during fitting. |
control |
a list of parameters for controlling the fitting
process. See the documentation for |
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 (and presently only) method |
x, y |
logical values indicating whether the response vector and design matrix used in the fitting process should be returned as components of the returned value. |
glm.obj |
an object of class |
dispersion |
the dispersion parameter for the fitting family.
By default the dispersion parameter is obtained from
|
correlation |
should the correlation matrix of the estimated parameters be printed. |
type |
the type of residuals which should be returned. The alternatives are:
|
glm returns an object of class glm
which inherits from the class lm.
The function summary can be used to obtain or print
a summary of the results and the function anova
and be used to produce and 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.
anova, coefficients, effects,
fitted.values,
lm,
residuals, summary.
## Annette Dobson (1990) "An Introduction to Statistical Modelling".
## Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3,9)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
anova(glm.D93)
summary(glm.D93)