SGPC: SGPC for Generalized Estimating Equations

Description Usage Arguments Value References See Also Examples

View source: R/geeglm.R

Description

Computes the Schwarz-type penalized Gaussian pseudo-likelihood criterion (SGPC) for one or more objects of the class glmgee.

Usage

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SGPC(..., verbose = TRUE)

Arguments

...

one or several objects of the class glmgee which are obtained from the fit of generalized estimating equations.

verbose

an (optional) logical switch indicating if should the report of results be printed. By default, verbose is set to be TRUE.

Value

A data.frame with the values of the gaussian pseudo-likelihood, the number of parameters in the linear predictor plus the number of parameters in the correlation matrix, and the value of SGPC for each glmgee object in the input.

References

Carey V.J. and Wang Y.-G. (2011) Working covariance model selection for generalized estimating equations. Statistics in Medicine 30, 3117–3124.

Zhu X. and Zhu Z. (2013) Comparison of criteria to select working correlation matrix in generalized estimating equations. Chinese Journal of Applied Probability and Statistics 29, 515-530.

Fu L., Hao Y. and Wang Y.-G. (2018) Working correlation structure selection in generalized estimating equations. Computational Statistics 33, 983-996.

See Also

QIC, CIC, RJC, GHYC, AGPC

Examples

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## Example 1
mod <- size ~ poly(days,4) + treat
fit1 <- glmgee(mod, id=tree, family=Gamma("log"), data=spruce, corstr="Exchangeable")
fit2 <- update(fit1, corstr="AR-1")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Independence")
SGPC(fit1, fit2, fit3, fit4)

## Example 2
mod <- dep ~ visit + group
fit1 <- glmgee(mod, id=subj, family=gaussian, corstr="Exchangeable", data=depression)
fit2 <- update(fit1, corstr="AR-1")
fit3 <- update(fit1, corstr="Non-Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Independence")
SGPC(fit1, fit2, fit3, fit4)

## Example 3
mod <- depressd ~ visit + group
fit1 <- glmgee(mod, id=subj, family=binomial, corstr="Exchangeable", data=depression)
fit2 <- update(fit1, corstr="AR-1")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Independence")
SGPC(fit1, fit2, fit3, fit4)

glmtoolbox documentation built on June 9, 2021, 9:07 a.m.

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