glmsimdata

Data simulated for variable selection under GLM framework

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

glmsimdata function

An object of class <code>"list"</code><dl>
 <dt>y</dt>
<dd>The outcome generated from Negative Binomial distribution with the dispersion parameter parameter=2</dd> <dt>x</dt>
<dd>A covariate matrix, of which the first column are all ones and rest columns contain normally distributed
 values</dd>
</dl>

Format

Data simulated for variable selection under GLM framework — glmsimdata

An object of class <code>"list"</code><dl>
 <dt>y</dt>
<dd>The outcome generated from Negative Binomial distribution with the dispersion parameter parameter=2</dd>

 <dt>x</dt>
<dd>A covariate matrix, of which the first column are all ones and rest columns contain normally distributed
 values</dd>


</dl>

glmsimdata: Data simulated for variable selection under GLM framework

Description

Usage

Arguments

Format

References

Examples