QIC {glmtoolbox} | R Documentation |
QIC for Generalized Estimating Equations
Description
Computes the quasi-likelihood under the independence model criterion (QIC) for one or more objects of the class glmgee.
Usage
QIC(
...,
k = 2,
u = FALSE,
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, |
u |
an (optional) logical switch indicating if QIC should be replaced by QICu. As default, |
verbose |
an (optional) logical switch indicating if should the report of results be printed. As default, |
digits |
an (optional) integer indicating the number of digits to print. As default, |
Value
A data.frame
with the values of -2*quasi-likelihood, the number of parameters in the linear predictor, and the value of QIC (or QICu if u
=TRUE) for each glmgee object in the input.
References
Pan W. (2001) Akaike's information criterion in generalized estimating equations, Biometrics 57:120-125.
Hin L.-Y., Carey V.J., Wang Y.-G. (2007) Criteria for Working–Correlation–Structure Selection in GEE: Assessment via Simulation. The American Statistician 61:360–364.
See Also
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")
QIC(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")
QIC(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")
QIC(fit1, fit2, fit3)