geesimdata

Data simulated for model selection under GEE framework without missingness

datasets

We developed a consistent and robust information criterion to conduct model selection for semiparametric models. It is free of distribution specification and powerful to locate the true model given large sample size. This package provides several usage of ELCIC with applications in generalized linear model (GLM), generalized estimating equation (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanism of missing at random and drop-out. Chixaing Chen, Ming Wang, Rongling Wu, Runze Li (2020) <doi:10.5705/ss.202020.0254>.

Chixiang Chen

ELCIC

The Empirical Likelihood-Based Consistent Information Criterion

Biyi Shen

Ming Wang

geesimdata function

An object of class <code>"list"</code><dl>
 <dt>y</dt>
<dd>The outcomes generated from Poisson distribution with three repeated measurements from each subject</dd> <dt>x</dt>
<dd>A covariate matrix, of which the first column are all ones and rest columns contain normally distributed. Two are time-dependent variables, and one is time-independent variable.</dd>
</dl>

Format

Data simulated for model selection under GEE framework without missingness — geesimdata

An object of class <code>"list"</code><dl>
 <dt>y</dt>
<dd>The outcomes generated from Poisson distribution with three repeated measurements from each subject</dd>

 <dt>x</dt>
<dd>A covariate matrix, of which the first column are all ones and rest columns contain normally distributed. Two are time-dependent variables, and one is time-independent variable.</dd>


</dl>

geesimdata: Data simulated for model selection under GEE framework without missingness

Description

Usage

Arguments

Format

References

Examples