MLIC.gee: MLIC and MLICC for Weighted GEE

In wgeesel: Weighted Generalized Estimating Equations and Model Selection

Description

Calculate the MLIC (missing longitudinal information criterion) for selection of mean model, and the MLICC (missing longitudinal information correlation criterion) for selection of working correlation structure, based on the expected quadratic loss and the WGEE.

Usage

MLIC.gee(object,object_full)

Arguments

object	a fitted model object of class "wgee".
object_full	a fitted model object of class "wgee": the largest candidate model under consideration to be fitted.

Value

Return a data frame of MLIC, MLICC and Wquad_loss.

Note

MLIC and MLICC model selection criterion for longitudinal data criterion with dropouts or monotone missingness under the assumption of MAR.

Author(s)

Cong Xu, Zheng Li and Ming Wang

References

Robins, J.M., Rotnitzky, A. and Zhao, L.P., 1995. Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90(429), pp.106-121.

Shen, C.W. and Chen, Y.H., 2012. Model selection for generalized estimating equations accommodating dropout missingness. Biometrics, 68(4), pp.1046-1054.

Shen, C.W. and Chen, Y.H., 2013. Model selection of generalized estimating equations with multiply imputed longitudinal data. Biometrical Journal, 55(6), pp.899-911.

See Also

wgee

Examples

data(imps)
fit1 <- wgee (Y ~ Drug+Sex+Time,data=imps,id=imps$ID,family="binomial",
             corstr="exchangeable",scale=NULL,mismodel= R ~ Drug+Time)

fit_f <- wgee (Y ~ Drug+Sex+Time+Time*Sex+Time*Drug,data=imps,id=imps$ID, family="binomial",
              corstr="exchangeable",scale=NULL,mismodel= R ~ Drug+Time)
###not run#####
##MLIC.gee(fit1,fit_f)

wgeesel documentation built on May 2, 2019, 3:41 a.m.