# evaluateGEEModel: The adaptive shrinkage estimate for generalized estimating... In seqest: Sequential Method for Classification and Generalized Estimating Equations Problem

## Description

`evaluateGEEModel` is used to get a generalized estimating equation of the data by the adaptive shrinkage estimate method.

## Usage

 ```1 2``` ```evaluateGEEModel(family, corstr, y, x, clusterID, criterion = "QIC", theta = 0.75, gamma = 1, leastVar = 3, mostVar = ncol(x)) ```

## Arguments

 `family` A description of the error distribution and link function to be used in the model. See family for details of `family` functions. `corstr` A character string specifying the correlation structure. The following are permitted: "independence", "exchangeable" and "ar1". `y` The response data. `x` A data frame contains the covariate vectors. `clusterID` The id for each subject in the initial samples. Note that the subjects in the same cluster will have identical id. `criterion` The model selection criteria, one of the 'PWD' or 'QIC'. `theta` The parameters of the adaptive shrinkage estimate. `gamma` The parameters of the adaptive shrinkage estimate. `leastVar` The minimum number of variables. `mostVar` The maximum number of variables.

## Details

evaluateGEEModel fits the current data by generalized estimating equations(GEE) according to the value of the family argument and the corstr argument. We should notice that this is not the ordinary generalized estimating equations. It can determine the variables that have an impact on the response which called effective variables. We use model selection criteria like the QIC criterion to choose the optimal value.

## Value

a list containing the following components

 `rho` the correlation coefficient of the clusters `beta` parameters that we estimate under the current samples `sandwich` the sandwich information matrix for covariance `nonZeroIdx` the index of the non zero coefficients `call` a list containing several matrices including the sandwich matrix

seqest documentation built on July 2, 2020, 2:28 a.m.