Produces an object of class `geese' which is a Generalized Estimating Equation fit of the clustered ordinal data.
ordgee(
formula = formula(data),
ooffset = NULL,
id,
waves = NULL,
data = parent.frame,
subset = NULL,
na.action = na.omit,
contrasts = NULL,
weights = NULL,
z = NULL,
mean.link = "logit",
corstr = "independence",
control = geese.control(...),
b = NA,
alpha = NA,
scale.fix = TRUE,
scale.val = 1,
int.const = TRUE,
rev = FALSE,
...
)a formula expression as for glm, of the form
response ~ predictors. See the documentation of lm and
formula for details. As for glm, this specifies the linear
predictor for modelling the mean. A term of the form
offset(expression) is allowed.
vector of offset for the odds ratio model.
a vector which identifies the clusters. The length of `id' should be the same as the number of observations. Data are assumed to be sorted so that observations on a cluster are contiguous rows for all entities in the formula.
an integer vector which identifies components in
clusters. The length of waves should be the same as the
number of observation. components with the same waves
value will have the same link functions.
an optional data frame in which to interpret the
variables occurring in the formula, along with the
id and n variables.
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.
a function to filter missing data. For gee
only na.omit should be used here.
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 full-rank 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.
an optional vector of weights to be used in the
fitting process. The length of weights should be the
same as the number of observations.
a design matrix for the odds ratio model. The number of rows of z is $$c^2 \sum n_i(n_i - 1)/2,$$ where \(n_i\) is the cluster size, and \(c\) is the number of categories minus 1.
a character string specifying the link function
for the means. The following are allowed: "logit",
"probit", and "cloglog".
a character string specifying the log odds. The
following are allowed: "independence",
"exchangeable", "unstructured", and
"userdefined".
a list of iteration and algorithmic constants. See
geese.control for their names and default
values. These can also be set as arguments to geese
itself.
an initial estimate for the mean parameters.
an initial estimate for the odds ratio parameters.
a logical variable indicating if scale is fixed; it is set at TRUE currently (it can not be FALSE yet!).
this argument is ignored currently.
a logical variable; if true, the intercepts are constant, and if false, the intercepts are different for different components in the response.
a logical variable. For example, for a three level ordered response Y = 2, the accumulated indicator is coded as (1, 0, 0) if true and (0, 1, 1) if false.
further arguments passed to or from other methods.
An object of class "geese" representing the fit.
Heagerty, P.J. and Zeger, S.L. (1996) Marginal regression models for clustered ordinal measurements. JASA, 91 1024--1036.
# NOT RUN {
data(respdis)
resp.l <- reshape(respdis, varying =list(c("y1", "y2", "y3", "y4")),
v.names = "resp", direction = "long")
resp.l <- resp.l[order(resp.l$id, resp.l$time),]
fit <- ordgee(ordered(resp) ~ trt, id=id, data=resp.l, int.const=FALSE)
summary(fit)
data(ohio)
ohio$resp <- ordered(as.factor(ohio$resp))
fit <- ordgee(resp ~ age + smoke + age:smoke, id = id, data=ohio)
summary(fit)
# }
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