This menu implements the General Estimating Equation (GEE) methodology of Liang and Zeger with quadratic estimation for the
covariance structure. In the terminology of Liang et al. the methodology implemented is a form of GEE1. For more details of
the implementation see the GEE procedure. GEE, as implemented here, is a comparatively simple non-likelihood method for fitting
marginal models to repeated measurements that can be used when the response has a distribution in the exponential family. This
includes the Gaussian distribution, for which the procedure implemented here reduces to a form of the EM algorithm, and then
produces exact ML or REML estimates, or a close approximation to these depending on the particular correlation structure
chosen. For other distributions the resulting estimates are not maximum likelihood but can be shown to have asymptotic properties
familiar from quasi-likelihood, such as consistency and asymptotic normality.
Available Data
This lists data structures appropriate to the current input field. The contents will change as you move from one field to the next.
Double-click on a name to copy it to the current input field; alternatively, you can type the name directly into the input field.
Response Variate
Provides a space for you to specify a response variate containing the data.
Distribution
List of available error distributions. If you select the binomial distribution then you must supply the binomial totals in the space provided.
Similarly, if the Negative
binomial distribution is selected you must supply the aggregation parameter in the space
provided.
Link Function
Lists the available link functions. If you select the power link function then you must supply the exponent in the space provided. Similarly,
if the Logratio link function is selected you can specify the parameter for logratio link in the form
log(mean/(mean+k)) using the Logratio field.
Model Terms
The model terms to be fitted are specified by entering a model formula.
Data
Allows you to specify the form of the data. The Subject Factor can be used when you have a factor to identify
the subjects. Alternatively, where the data consist of outcomes and numbers with those outcomes, you can select
Outcomes and supply a factor specifying the outcomes and a variate containing the numbers with those outcomes.
Times
A factor specifying the times of the repeated measurements.
Operators
This provides a quick way of entering operators in the Model Terms formula. Double-click on the required symbol to copy it to the current
input field. You can also type in operators directly. See model formula for a description of each.