Description Usage Arguments Details Value Side Effects Note References See Also Examples
Produces an object of class "gee"
which is a Generalized Estimation
Equation fit of the data.
1 2 3 4 5 6 7 
formula 
a formula expression as for other regression models, of the form

id 
a vector which identifies the clusters. The length of 
data 
an optional data frame in which to interpret the variables occurring
in the 
subset 
expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default. 
na.action 
a function to filter missing data. For 
R 
a square matrix of dimension maximum cluster size containing the user
specified correlation. This is only appropriate if 
b 
an initial estimate for the parameters. 
tol 
the tolerance used in the fitting algorithm. 
maxiter 
the maximum number of iterations. 
family 
a 
corstr 
a character string specifying the correlation structure.
The following are permitted:

Mv 
When 
silent 
a logical variable controlling whether parameter estimates at each iteration are printed. 
contrasts 
a list giving contrasts for some or all of the factors appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any fullrank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels. 
scale.fix 
a logical variable; if true, the scale parameter is fixed at
the value of 
scale.value 
numeric variable giving the value to which the scale parameter
should be fixed; used only if 
v4.4compat 
logical variable requesting compatibility of correlation parameter estimates with previous versions; the current version revises to be more faithful to the Liang and Zeger (1986) proposals (compatible with the Groemping SAS macro, version 2.03) 
Though input data need not be sorted by the variable
named "id"
, the program
will interpret physically contiguous records possessing the
same value of id
as members of the same cluster. Thus it
is possible to use the following vector as an id
vector
to discriminate 4 clusters of size 4:
c(0,0,0,0,1,1,1,1,0,0,0,0,1,1,1,1)
.
An object of class "gee"
representing the fit.
Offsets must be specified in the model formula, as in glm
.
This is version 4.8 of this user documentation file, revised 98/01/27. The assistance of Dr B Ripley is gratefully acknowledged.
Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73 13–22.
Zeger, S.L. and Liang, K.Y. (1986) Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42 121–130.
1 2 3 4 5 6 7 8 9 10 11 12 13  data(warpbreaks)
## marginal analysis of random effects model for wool
summary(gee(breaks ~ tension, id=wool, data=warpbreaks, corstr="exchangeable"))
## test for serial correlation in blocks
summary(gee(breaks ~ tension, id=wool, data=warpbreaks, corstr="ARM", Mv=1))
if(require(MASS)) {
data(OME)
## not fully appropriate link for these data.
(fm < gee(cbind(Correct, TrialsCorrect) ~ Loud + Age + OME, id = ID,
data = OME, family = binomial, corstr = "exchangeable"))
summary(fm)
}

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