AGPC: AGPC for Generalized Estimating Equations

In glmtoolbox: Set of Tools to Data Analysis using Generalized Linear Models

AGPC for Generalized Estimating Equations

Description

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

Usage

AGPC(..., k = 2, verbose = TRUE, digits = max(3, getOption("digits") - 2))

Arguments

...	one or several objects of the class glmgee.
k	an (optional) non-negative value giving the magnitude of the penalty. As default, k is set to 2.
verbose	an (optional) logical switch indicating if should the report of results be printed. As default, verbose is set to TRUE.
digits	an (optional) integer indicating the number of digits to print. As default, digits is set to max(3, getOption("digits") - 2).

Details

If k is set to 0 then the AGPC reduces to the Gaussian pseudo-likelihood criterion (GPC), proposed by Carey and Wang (2011), which corresponds to the logarithm of the multivariate normal density function.

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 AGPC for each glmgee object in the input.

References

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

Zhu X., 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., Wang Y.-G. (2018) Working correlation structure selection in generalized estimating equations. Computational Statistics 33:983-996.

Vanegas L.H., Rondon L.M., Paula G.A. (2023) Generalized Estimating Equations using the new R package glmtoolbox. The R Journal 15:105-133.

See Also

QIC, CIC, RJC, GHYC, SGPC

Examples

###### Example 1: Effect of ozone-enriched atmosphere on growth of sitka spruces
data(spruces)
mod1 <- size ~ poly(days,4) + treat
fit1 <- glmgee(mod1, id=tree, family=Gamma(log), data=spruces)
fit2 <- update(fit1, corstr="AR-M-dependent")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Exchangeable")
AGPC(fit1, fit2, fit3, fit4)

###### Example 2: Treatment for severe postnatal depression
data(depression)
mod2 <- depressd ~ visit + group
fit1 <- glmgee(mod2, id=subj, family=binomial(logit), data=depression)
fit2 <- update(fit1, corstr="AR-M-dependent")
fit3 <- update(fit1, corstr="Stationary-M-dependent(2)")
fit4 <- update(fit1, corstr="Exchangeable")
AGPC(fit1, fit2, fit3, fit4)

###### Example 3: Treatment for severe postnatal depression (2)
mod3 <- dep ~ visit*group
fit1 <- glmgee(mod3, id=subj, family=gaussian(identity), data=depression)
fit2 <- update(fit1, corstr="AR-M-dependent")
fit3 <- update(fit1, corstr="Exchangeable")
AGPC(fit1, fit2, fit3)

glmtoolbox documentation built on Sept. 11, 2024, 7:32 p.m.