wgeesimdata

Data simulated for model selection under WGEE framework for missing longitudinal data under the mechanism of missing at random and drop-out.

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

wgeesimdata function

An object of class <code>"list"</code><dl>
 <dt>y</dt>
<dd>The outcomes generated from Bernoulli distribution with three repeated measurements from each subject. The data is longitudinal dropout and missing at random.</dd> <dt>x</dt>
<dd>A full covariate matrix. The first column corresponds to the intercept; the second column contains time-independent continuous-scale variable x1; the third column contains a doctor-visit variable x2; the third column contains time-independent binary-scale variable x3.</dd> <dt>x_mis</dt>
<dd>A covariate matrix for missing data model. The first column corresponds to the intercept; the second column contains continuous variable x_mis1.</dd> <dt>id</dt>
<dd>A vector indicating subject id.</dd> <dt>obs_ind</dt>
<dd>A vector indicating observations for each record: 1 for observed records, and 0 for unobserved records.</dd>
</dl>

Format

Data simulated for model selection under WGEE framework for missing longitudinal data under the mechanism of missing at random and drop-out. — wgeesimdata

An object of class <code>"list"</code><dl>
 <dt>y</dt>
<dd>The outcomes generated from Bernoulli distribution with three repeated measurements from each subject. The data is longitudinal dropout and missing at random.</dd>

 <dt>x</dt>
<dd>A full covariate matrix. The first column corresponds to the intercept; the second column contains time-independent continuous-scale variable x1; the third column contains a doctor-visit variable x2; the third column contains time-independent binary-scale variable x3.</dd>

 <dt>x_mis</dt>
<dd>A covariate matrix for missing data model. The first column corresponds to the intercept; the second column contains continuous variable x_mis1.</dd>

 <dt>id</dt>
<dd>A vector indicating subject id.</dd>

 <dt>obs_ind</dt>
<dd>A vector indicating observations for each record: 1 for observed records, and 0 for unobserved records.</dd>


</dl>

wgeesimdata: Data simulated for model selection under WGEE framework for missing longitudinal data under the mechanism of missing at random and drop-out.

Description

Usage

Arguments

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References

Examples